Metabolic marker composition for early diagnosis of cervical cancer and screening method and application thereof

Abstract

The application discloses a metabolic marker composition for early cervical cancer diagnosis and a screening method and application thereof. The metabolic marker composition for early cervical cancer diagnosis comprises pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid. The metabolic marker composition provided by the application has high sensitivity and specificity when used for early cervical cancer diagnosis, can realize precise screening of early cervical cancer, and provides important help for prevention of cervical cancer and reduction of morbidity. Meanwhile, the metabolic marker composition is simple in operation, convenient in sample acquisition, low in cost, and non-invasive when used for early cervical cancer diagnosis, so that patients can more conveniently, quickly and safely receive detection, and is more suitable for large-scale population screening and regular tracking in areas with tight medical resources.

Description

TECHNICAL FIELD

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

[0002] Cervical cancer (CC) is a malignant tumor that occurs in the vaginal part of the cervix and the cervical canal, and is one of the most common gynecological malignancies, with a high incidence 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 lower abdominal pain, vaginal bleeding, and abnormal vaginal discharge may occur, and when these symptoms occur, the patient usually seeks medical attention in the late stage. The treatment effect of cervical cancer in the late stage is usually worse than that in the early stage, because the late-stage cancer may have spread to other parts outside the cervix, such as the uterine tissue, pelvic wall, vagina, or distant organs, which seriously affects the prognosis and increases the treatment cost, and also brings certain physical and psychological pain to the patient during the treatment, so the screening of early cervical cancer is particularly important.

[0003] Human papillomavirus (HPV) is a common sexually transmitted virus, and there are many different types, 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 a woman is infected with a high-risk HPV, the virus may persist in the cervical area and cause abnormal cell proliferation, which may develop into cervical cancer. Therefore, HPV screening combined with cytological screening as the main detection method for cervical cancer screening is widely used for early screening and regular follow-up of cervical cancer. Although HPV testing has high sensitivity for high-risk HPV infection, it may also produce 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 in low-risk HPV infection. In addition, HPV screening as an invasive detection method reduces patient compliance, and HPV testing is usually more expensive, which may increase the burden on the medical system, especially in resource-limited areas, and high costs may prevent some people from regularly undergoing HPV testing.

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

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

[0006] The technical scheme of the present application is as follows:

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

[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 further comprises 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 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.

[0011] In a second aspect of the present application, a screening method for the metabolic marker composition for early cervical cancer diagnosis of the present application is provided, comprising the following steps:

[0012] Extracting the plasma of early cervical cancer patients and healthy people respectively to obtain an extract;

[0013] Detecting the organic phase and the aqueous phase in the extract by using a chromatography-mass spectrometry instrument to obtain detection data;

[0014] Performing data processing on the detection data, and then identifying the metabolites to obtain metabolomics data;

[0015] Analyzing the metabolomics data to screen the metabolic marker composition for early cervical cancer diagnosis.

[0016] Optionally, the step of analyzing the metabolomics data to screen the metabolic marker composition for early cervical cancer diagnosis comprises:

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

[0018] Optionally, the metabolic marker composition for early cervical cancer diagnosis is screened with the screening conditions of an area under the receiver operating characteristic curve greater than 0.75 and a VIP value greater than 1.8.

[0019] In a third aspect, the present application provides use of the metabolic marker composition for early cervical cancer diagnosis of the present application in the preparation of a product for diagnosing early cervical cancer.

[0020] Optionally, the sample used in the product for diagnosing early cervical cancer comprises 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 metabolic marker composition provided by the present application has high sensitivity and specificity when used for early cervical cancer diagnosis, and can realize precise screening of early cervical cancer, providing important help for prevention and reduction of incidence of cervical cancer. At the same time, the metabolic marker composition for early cervical cancer diagnosis is simple to operate and convenient to obtain samples, with low cost and no invasiveness, so that patients can more conveniently, quickly and safely receive detection, and it is more suitable for large-scale population screening and regular tracking in areas with insufficient medical resources. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 Multivariate ROC curve analysis diagram of 11 metabolic markers for distinguishing between healthy control group and early cervical cancer group in modeling group of Example 3.

[0024] Figure 2 Multivariate ROC curve analysis diagram of 11 metabolic markers for distinguishing between healthy control group and early cervical cancer group in verification group of Example 3. DETAILED DESCRIPTION

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

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

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

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

[0029] In lysophosphatidylcholine 20:3, 20:3 refers to the fatty acid side chain containing 20 carbon atoms and 3 double bonds.

[0030] In lysophosphatidylcholine 22:4, 22:4 refers to the fatty acid side chain containing 22 carbon atoms and 4 double bonds.

[0031] In lysophosphatidylcholine 22:5, 22:5 refers to the fatty acid side chain containing 22 carbon atoms and 5 double bonds.

[0032] Lysophosphatidylethanolamine belongs to glycerophospholipids and is an enzymatic product of phosphatidylethanolamine after hydrolysis by phospholipase, thus containing only one fatty acid side chain.

[0033] In lysophosphatidylethanolamine 20:4, 20:4 refers to the fatty acid side chain containing 20 carbon atoms and 4 double bonds.

[0034] Methylphosphatidylcholine is formed after phosphatidylcholine is subjected to methylation reaction and contains two fatty acid side chains.

[0035] In methylphosphatidylcholine 30:3e, 30:3 refers to the two fatty acid side chains containing 30 carbon atoms and 3 double bonds, and e indicates that one of the two fatty acid side chains originally esterified with one hydroxyl and one fatty acid molecule to form a covalent ester bond is replaced by an ether bond, thus lacking one oxygen atom.

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

[0037] In phosphatidylcholine 32:2, 32:2 refers to the other two fatty acid side chains containing 32 carbon atoms and 2 double bonds.

[0038] Metabolomics technology is widely used in early diagnosis of diseases, individualized medicine, new drug research and development, and target discovery, etc. In recent years, it has rapidly developed and has broad prospects in the field of disease diagnosis. Metabolomics can find biomarkers related to diseases by analyzing the changes of metabolites in body fluids (such as blood, urine), and the changes of these biomarkers often occur earlier than traditional clinical symptoms, which is more helpful for early diagnosis of diseases. Therefore, based on metabolomics data, the present application obtains early cervical cancer diagnosis markers with high sensitivity and high specificity, which provides effective help for the early diagnosis of early cervical cancer, and achieves early screening, early discovery and early treatment. Specifically, the present application provides a metabolic marker composition for early cervical cancer diagnosis, wherein the metabolic marker composition for early cervical cancer diagnosis comprises pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid.

[0039] In the embodiments of the present application, the metabolic marker composition for early cervical cancer diagnosis containing four metabolic markers is significantly different between early cervical cancer patients and healthy people, and can effectively distinguish early cervical cancer patients from healthy people. The metabolic marker composition provided by the embodiments of the present application has high sensitivity and specificity when used for early cervical cancer diagnosis, and can realize accurate screening of early cervical cancer, which provides important help for the prevention and reduction of the incidence of cervical cancer. At the same time, the metabolic marker composition for early cervical cancer diagnosis is simple to operate, convenient to obtain samples, low in cost, and non-invasive, so that patients can more conveniently, quickly and safely receive detection, and it is more suitable for large-scale population screening and regular tracking in areas with insufficient medical resources.

[0040] In some embodiments, the metabolic marker composition for early cervical cancer diagnosis further comprises at least one of lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4 and hippuric acid. That is, in the present embodiment, the metabolic marker composition for early cervical cancer diagnosis comprises 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 cervical cancer diagnosis provided by the present 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 comprises 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 early cervical cancer diagnosis comprises 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 early cervical cancer diagnosis comprises 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 by the present 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 by the present 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 embodiment also provides a screening method for the metabolic marker composition for early cervical cancer diagnosis as described above, which comprises the following steps:

[0044] S1, extracting the plasma of early cervical cancer patients and healthy people respectively to obtain an extract;

[0045] S2, detecting the organic phase and the aqueous phase in the extract by using a chromatography-mass spectrometry instrument to obtain detection data;

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

[0047] S4, analyzing the metabolomics data to screen the metabolic marker composition for early cervical cancer diagnosis.

[0048] The embodiment is based on metabolomics technology, detects and identifies small molecule metabolites in plasma of early cervical cancer patients and healthy people, screens a specific metabolic marker composition for early cervical cancer diagnosis, and has high sensitivity and specificity for early cervical cancer diagnosis. In addition, the metabolic marker composition for early cervical cancer diagnosis has high specificity and sensitivity for early cervical cancer diagnosis, high accuracy, can make patients more convenient and safe to receive detection, and is suitable for screening and detection of large-scale general population.

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

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

[0051] In some embodiments, the metabolic marker composition for early cervical cancer diagnosis is screened with the screening conditions of area under the receiver operating characteristic curve greater than 0.75 and VIP value greater than 1.8.

[0052] The embodiment of the present application also provides a use of the metabolic marker composition for early cervical cancer diagnosis of the present application in preparing a product for diagnosing early cervical cancer.

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

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

[0055] The present application will be further described below through specific embodiments.

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

[0057] 1. Subject condition and sample collection

[0058] Early cervical cancer sample inclusion criteria:

[0059] The subject must meet all the following inclusion criteria:

[0060] (1) Female aged ≥18 years;

[0061] (2) Read and fully understand, sign the informed consent form, and can provide blood samples for metabolomics detection;

[0062] (3) diagnosed as primary cervical cancer by biopsy / postoperative pathology or clinical diagnosis by comprehensive assessment of clinicians, and included in the early cervical cancer group according to clinical stage information.

[0063] Exclusion criteria for early cervical cancer samples:

[0064] Early cervical cancer subjects were excluded if they met any of the following exclusion criteria:

[0065] (1) pregnancy or lactation period;

[0066] (2) emergency or rescue;

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

[0068] (4) organ transplant or previous non-autologous (allogeneic) bone marrow or stem cell transplant;

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

[0070] (6) multiple primary malignant tumors.

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

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

[0073] Table 1, subject information

[0074] Healthy control (HC) group Early cervical cancer (eCC) group Number of subjects in modeling group (persons) 93 55 Number of subjects in validation group (persons) 30 26 Total (persons) 123 81

[0075] 2. Reagents

[0076] Mass spectrometry grade methanol, acetonitrile, water, acetic acid and isopropanol, and chromatography (HPLC) grade formic acid, ammonium acetate and methyl tert-butyl ether were purchased from Sigma-Aldrich Company, USA.

[0077] 3. Preparation of sample

[0078] Take 100 μL of plasma and place it in 1000 μL of pre-cooled mixed solution (consisting of methyl tert-butyl ether and methanol, with a volume ratio of methyl tert-butyl ether to methanol being 3:1), vortex to mix, and obtain a sample extract.

[0079] Add 500 μL of a mixed solution of methanol and water (with a volume ratio of methanol to water being 3:1) to the sample extract, ultrasonicate, stand still, vortex, and centrifugally separate the layers. After the sample is separated into layers, the upper layer is the organic phase, and the lower layer is 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 (with a volume ratio of acetonitrile to isopropanol being 3:1), and incubate at room temperature for 15 minutes. After incubation, vortex the centrifuge tube to mix, ultrasonicate for 5 minutes, and then centrifuge the centrifuge tube at 12000 rpm at room temperature for 5 minutes. Take 180 μL of supernatant from the centrifuge tube into a 2 mL glass sample vial as the organic phase to be tested, and perform detection 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 is precipitated in the centrifuge tube, centrifuge the centrifuge tube, transfer 1000 μL of supernatant to a new centrifuge tube, and dry overnight. Add 200 μL of water to the dried centrifuge tube, and incubate at room temperature for 15 minutes. After incubation, vortex the centrifuge tube to mix, ultrasonicate for 5 minutes, and then centrifuge the centrifuge tube at 12000 rpm at room temperature for 5 minutes. Take 180 μL of supernatant from the centrifuge tube into a 2 mL glass sample vial as the aqueous phase to be tested, and perform detection on a machine (LC-MS).

[0082] 4. Detection of small molecule metabolites

[0083] The organic phase uses a Waters ACQUTTY UPLC BEH C8 1.7 μm 2.1x100mm column (column), and the aqueous phase uses a Waters ACQUTTY UPLC HSS T3 1.8 μm 2.1x100mm column (column) to separate small molecules; both the liquid chromatograph and the mass spectrometer use an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometer (Thermo Fisher Scientific).

[0084] The mobile phase parameters are as follows:

[0085] The flow phase parameters of the organic phase test solution are as follows: the flow phase A is a water 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%); the flow phase B is a mixed solution of acetonitrile and isopropyl alcohol containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile to isopropyl alcohol 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: 55%-89% flow phase B for 0-12 minutes, and 100% flow phase B for 12-19.5 minutes.

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

[0087] The mass spectrometry parameters are as follows:

[0088] The mass spectrometry data are collected in Full MS and Full MS / dd-MS2 modes (each containing positive and negative modes), and the parameters used by QExactive are as follows:

[0089] In the Full MS mode, the resolution is 70,000, the scanning range is 100-1500 m / z (m / z is the ratio of ion mass to charge number), AGC (automatic gain control) is 3E+6 (i.e. 3x10 6 ), and the maximum IT (maximum injection time) is 200 milliseconds;

[0090] In the Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometry is 17,500, the quadrupole rod window is 1.5 m / z, AGC is 1E+5 (i.e. 1x10 5 ), the ion maximum injection time is 50 milliseconds, and the HCD (high-energy collisional dissociation) relative collision energy is 30 eV.

[0091] 5. Metabolomics data preprocessing and metabolite identification

[0092] (1) Metabolomics data processing

[0093] a. The RAW format file after mass spectrometry is extracted into a FeatureXML format file, the dimension of the original mass spectrometry data is reduced, and the signal-to-noise ratio is improved;

[0094] b. The retention time of the peak format data after extraction is corrected and aligned between samples by using the peak alignment algorithm of OpenMS software, so as to convert the mass spectrometry data into a data matrix;

[0095] c. Matching and filtering the isotope peaks in the data matrix obtained in step b, and replacing abnormal data (0, negative value, background noise, etc.) with null values;

[0096] d. Removing the characteristic peaks with a detection rate < 80% from all the characteristic peaks obtained in step c, filling the characteristic peaks with a detection rate > 80% with the median value of the characteristic peak, and adding 5% random noise (subject to standard normal distribution);

[0097] e. In order to reduce the differences in metabolite concentrations between samples and make the data distribution more symmetrical, normalization autoencoder (NormAE) was used for homogenization processing to remove batch effects and other systematic errors.

[0098] (2) Identification of metabolites

[0099] According to the spectrum information of the compound primary parent ion (MS1) and secondary fragment ion (MS2) obtained after software analysis of the original data, the mass-to-charge ratio (m / z) of the primary mass spectrum and the fragment of the secondary ion are matched with the spectrum information of the primary and secondary metabolites in the public database to qualitatively identify the metabolites. Commonly used metabolite databases include human metabolite database (HMDB, www.hmdb.ca), metabolomics database (Metlin, metlin.scripps.edu), mass spectrum database (www.massbank.jp) and lipid metabolite database (Lipidmap, www.lipidmaps.org). The metabolites identified based on the related database are finally verified according to the retention time, MS1 and MS2 mass spectrum information of the standard product under the same chromatographic column and mass spectrometry conditions. The standard for metabolite identification is that the retention time is within 0.1 min difference, and the theoretical value and the measured value of the molecular weight of the metabolite are less than 10ppm.

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

[0101] Firstly, univariate ROC (Receiver Operating Characteristic Curve) analysis and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were performed on the data of the modeling group (including HC group and eCC group, the number of HC group was 93, and the number of eCC group was 55) respectively, and univariate AUC value (i.e. univariate ROC curve area value) and VIP value (i.e. variable importance value) were obtained. Secondly, using univariate AUC value > 0.75 and VIP value > 1.8 as screening conditions, a total of 11 metabolic markers with significant contribution to the difference between groups (Table 2) were finally determined as important metabolic markers for distinguishing between healthy and early cervical cancer.

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

[0103]

[0104]

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

[0106] In order to verify the diagnostic effect of the screened 11 metabolic markers in distinguishing between healthy and early cervical cancer, multivariate ROC curve analysis was performed on the above-mentioned 11 metabolic markers 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 selected as the training set, and 1 / 4 was randomly selected as the test set for learning, and the machine learning support vector machine (SVM) was randomly cycled for 1000 times, and by statistically averaging the average value of the final model accuracy, a diagnostic model for distinguishing between healthy and early cervical cancer was constructed.

[0107] The ROC curve is a method for studying the relationship between sensitivity and specificity of the model, with sensitivity as the vertical coordinate and 1-specitivity as the horizontal coordinate. The evaluation is to compare the area under the curve (AUC), and when AUC is greater than 0.5, the closer AUC is to 1, the better the model performance, which means the better the diagnostic effect. If less than 0.5, it means that the accuracy of the model is not good. In addition to the common parameters such as receiver operating curve (ROC) and area under the curve (AUC), the ROC classification prediction model also includes sensitivity (Sensitivity) and specificity (Specificity).

[0108] Sensitivity is:

[0109]

[0110] Specificity is:

[0111]

[0112] wherein,

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

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

[0115] FP (False Positive): false positive, the number of samples actually negative examples incorrectly predicted as positive examples;

[0116] FN (False Negative): false negative, the number of samples actually positive examples incorrectly predicted as negative examples.

[0117] The results are shown in Figure 1 The results show that the diagnostic model has high diagnostic performance.

[0118] 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. Multivariate ROC curve analysis was performed to evaluate the independent verification effect of the diagnostic model on the unknown data set (i.e. validation group data set) other than the modeling group data set. After putting the validation group samples into the diagnostic model constructed by the modeling group, according to the detection data of 11 important metabolic markers for distinguishing between healthy and early cervical cancer of each sample, the corresponding probability value (Probability) will be output. According to the probability value of each sample as the diagnostic threshold, a set of confusion matrix (including true positive, true negative, false positive and false negative) is obtained. According to the formula, the sensitivity and specificity can be calculated, and a point can be marked in the ROC analysis graph with sensitivity as the vertical coordinate and 1-specificity as the horizontal coordinate. Similarly, when the probability value of each sample is used as the diagnostic threshold, a plurality of different points are obtained in the ROC analysis graph, and connecting these points will draw a ROC curve graph. 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 of Table 3, in the diagnostic model constructed based on the above 11 metabolic markers, with a diagnostic threshold of 0.4583, 24 of the 26 patients with early cervical cancer were judged to be early cervical cancer, and 2 were misjudged as healthy people; among the 30 healthy subjects, 26 were correctly judged, and 4 were misjudged as early cervical cancer; the ROC analysis results of the diagnostic model in the validation group are shown in Table 4. Figure 2 As shown in the confusion matrix results of Table 3, in the diagnostic model constructed based on the above 11 metabolic markers, with a diagnostic threshold of 0.4583, 24 of the 26 patients with early cervical cancer were judged to be early cervical cancer, and 2 were misjudged as healthy people; among the 30 healthy subjects, 26 were correctly judged, and 4 were misjudged as early cervical cancer; the ROC analysis results of the diagnostic model in the validation group are shown in Table 4.

[0120] Table 3, Confusion matrix of the diagnostic model for distinguishing 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] The diagnostic model using 7 metabolic marker combinations was analyzed by ROC curve in the modeling group, and the difference from Example 3 was that 7 metabolic markers, i.e., pyroglutamic acid, lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4, hippuric acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid, were combined to construct the diagnostic model.

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

[0125] To further verify the effectiveness of the diagnostic model for distinguishing between healthy and early cervical cancer based on the data of the modeling group, the validation was performed in the validation group, and 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 4 metabolic markers

[0127] The diagnostic model using 4 metabolic marker combinations was analyzed by ROC curve in the modeling group, and the difference from Example 3 was that 4 metabolic markers, i.e., pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid, were combined to construct the diagnostic model.

[0128] The results show that when using 4 metabolic markers, 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 constructed based on the modeling group data, verification was performed in the verification group, and multivariate ROC curve analysis was performed. The results show that in the verification group, AUC = 0.958 (sensitivity = 0.885, specificity = 0.867).

[0130] The above results show that the constructed diagnostic model has high diagnostic performance and has clinical diagnostic significance. The above constructed diagnostic model for distinguishing between healthy and early cervical cancer also has good diagnostic effect in the verification group.

[0131] In summary, the present application provides a metabolic marker composition for early cervical cancer diagnosis and a screening method and application thereof. The metabolic marker composition has high sensitivity and specificity when used for early cervical cancer diagnosis, can realize precise screening of early cervical cancer, and provides important help for prevention and reduction of the incidence of cervical cancer. At the same time, the metabolic marker composition is simple and convenient to obtain when used for early cervical cancer diagnosis, has low cost, is non-invasive, enables patients to receive detection more conveniently, quickly and safely, and is more suitable for large-scale population screening and regular tracking in areas with insufficient medical resources.

[0132] It should be understood that the application of the present application is not limited to the above examples, and those skilled in the art can make improvements or changes according to the above description, and all these improvements and changes shall belong to the protection scope of the appended claims of the present application.

Claims

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

2. A metabolic biomarker composition for the early diagnosis of cervical cancer, characterized in that, The metabolic marker composition for early cervical cancer diagnosis consists of pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2, pyruvate, lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4, and hippuric acid.

3. A metabolic biomarker composition for early cervical cancer diagnosis, characterized in that, The metabolic marker composition for early cervical cancer diagnosis comprises pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2, pyruvate, lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4, hippuric acid, 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4, and L-valine.

4. A method for screening a metabolic biomarker composition for early cervical cancer diagnosis according to any one of claims 1-3, characterized in that, Includes the following steps: 100 μL of plasma from patients with early-stage cervical cancer and healthy individuals were placed in 1000 μL of a pre-cooled mixed solution of methyl tert-butyl ether and methanol in a volume ratio of 3:1, and vortexed to obtain the sample extract. Add 500 μL of a 3:1 mixture of methanol and water to the sample extract, sonicate, let stand, vortex and centrifuge to separate the layers. After separation, the upper layer is the organic phase and the lower layer is the aqueous phase. The organic and aqueous phases in the extract were detected using chromatography-mass spectrometry (GC-MS) to obtain detection data. The organic phase was analyzed using a Waters ACQUTTY UPLC BEH C8 1.7μm 2.1×100mm column, and the aqueous phase was analyzed using a Waters ACQUTTY UPLC HSS T3 1.8μm 2.1×100mm column. The mobile phase parameters for the organic phase analyte are as follows: Mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate by mass; Mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 1% ammonium acetate by mass, with a volume ratio of acetonitrile to isopropanol of 7:3; The separation and elution gradient is as follows: 55%-89% mobile phase B for 0-12 minutes, and 100% mobile phase B for 12-19.5 minutes; The mobile phase parameters of the aqueous test solution are as follows: Mobile phase A is an aqueous solution containing 0.1% formic acid by mass; Mobile phase B is an acetonitrile solution containing 0.1% formic acid by mass; The separation and elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, and 13-18 minutes is 99% mobile phase B; The mass spectrometry parameters are as follows: Mass spectrometry data were acquired in both positive and negative modes using Full MS and Full MS / dd-MS2. The parameters used for Q Exactive are as follows: In Full MS mode, the resolution is 70,000 m / s, the scan range is 100-1500 m / s, and the automatic gain control is 3 × 10⁻⁶ m / s. 6 The maximum ion implantation time is 200 ms; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5 m / z, and the automatic gain control is 1 × 10⁻⁶. 5 The maximum ion implantation time is 50 milliseconds, and the relative collision energy for high-energy collision dissociation is 30 eV. The detection data is processed and then the metabolites are identified to obtain metabolomics data. The metabolomics data were subjected to univariate receiver operating characteristic (ROC) curve analysis and orthogonal partial least squares discriminant analysis. The metabolic biomarker composition for early cervical cancer diagnosis was obtained by screening based on the ROC curve area under the curve being greater than 0.75 and the VIP value being greater than 1.

8.

5. The use of the metabolic marker composition for early cervical cancer diagnosis according to any one of claims 1-3 in the preparation of a product for diagnosing early cervical cancer.

6. The application according to claim 5, characterized in that, The product is used in the diagnosis of early cervical cancer and the samples used include at least one of serum, plasma, blood, and dried blood smears.

7. The application according to claim 5, characterized in that, The products include reagents or kits.

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