Specific metabolism marker combination for diagnosing ovarian cancer and application thereof

Abstract

The invention discloses a specific metabolic marker combination for diagnosing ovarian cancer and application thereof, and belongs to the field of markers for detecting ovarian cancer. The invention discloses a specific metabolic marker combination for diagnosing ovarian cancer. The specific metabolic marker combination comprises phosphatidylcholine 36: 6, sphingomyelin d40: 2 and sphingomyelin d41: 2. According to the invention, a metabonomics technology is used to screen out a specific metabolism marker combination for diagnosing ovarian cancer, and the specific metabolism marker combination has high sensitivity in ovarian cancer diagnosis and has high diagnosis capability for ovarian cancer diagnosis. The specific metabolic marker combination also has high specificity in ovarian cancer diagnosis, but is not applicable to diagnosis of endometrial cancer and cervical cancer. In addition, the plasma sample acquisition mode has the advantages of non-invasiveness and convenience, and has a wide application prospect in the research of biomarkers.

Description

Technical Field

[0001] The present invention relates to the field of markers for detecting ovarian cancer, and in particular to a specific metabolic marker combination for diagnosing ovarian cancer and applications thereof. Background Art

[0002] Ovarian cancer (OC) is a common and fatal gynecological malignancy. In my country, the incidence of OC ranks third among all female reproductive system diseases and is showing an increasing trend year by year. Its mortality rate is the highest among all female reproductive system diseases, making it a serious threat to women's health. The ovaries are located deep in the pelvic cavity, and early ovarian lesions often have no specific clinical symptoms. By the time symptoms appear and patients seek medical attention, 70% of patients are already in the late stage, having missed the optimal treatment opportunity. Therefore, the search for OC-specific metabolites with high specificity and sensitivity is of great significance for achieving early diagnosis of ovarian cancer and improving patient survival rates.

[0003] Currently, conventional screening and diagnostic methods for ovarian cancer primarily include tumor marker testing (such as CA125 and HE4) and imaging studies (such as ultrasound, CT, and MRI). However, these methods have significant limitations in sensitivity and specificity. Although CA125 is a widely used tumor marker, its sensitivity is low in the early stages of ovarian cancer and it can also be elevated in benign conditions such as endometriosis and pelvic inflammatory disease, leading to the risk of misdiagnosis and missed diagnosis. Although HE4 has improved specificity, it is less effective in some ovarian cancer subtypes. Furthermore, imaging studies are limited in their ability to identify early, small lesions, further limiting early diagnosis of ovarian cancer. The gold standard for diagnosing ovarian cancer is surgical histopathological examination. While this method has high accuracy, it is invasive, expensive, and unsuitable for large-scale population screening.

[0004] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a specific metabolic marker combination for diagnosing ovarian cancer and its application, aiming to solve the problem that the existing diagnostic methods have obvious limitations in sensitivity and specificity.

[0006] Metabolomics, an emerging systems biology research approach, can keenly detect metabolic abnormalities in the early stages of disease by analyzing the dynamic changes in small molecule metabolites within an organism. Tumor cells undergo significant metabolic reprogramming during their growth and spread. Analyzing metabolites in body fluids such as blood and urine can keenly detect metabolic abnormalities in the early stages of disease, providing the potential for early diagnosis. Studies have shown that because ovarian, cervical, and endometrial cancers are all gynecological reproductive system tumors, while they share similarities in metabolic manifestations, each cancer still possesses unique metabolite signatures. The identification of specific metabolites not only helps differentiate between different gynecological cancer types but also provides a basis for developing personalized treatment plans, assessing patient response to specific treatment options, optimizing treatment strategies, and improving therapeutic efficacy. Specific metabolites can also be used to monitor disease progression and prognosis. Regular monitoring of specific metabolite levels can help assess treatment efficacy and prognosis, allowing for timely adjustments to treatment plans. Furthermore, identifying ovarian cancer-specific metabolites can advance related research and drug development. Understanding the biological functions and mechanisms of action of these metabolites can provide clues for the discovery of new drug targets. Therefore, finding ovarian cancer-specific metabolites is of great significance for achieving early diagnosis, improving diagnostic accuracy, guiding personalized treatment, and monitoring disease progression.

[0007] The present invention aims to utilize metabolomics technology to screen specific metabolic markers for ovarian cancer and explore their potential for early diagnosis. Furthermore, by integrating the metabolic characteristics of various gynecological tumors, including ovarian cancer, cervical cancer, and endometrial cancer, a panel of highly specific and sensitive ovarian cancer-specific metabolic markers will be established for early screening and diagnosis of ovarian cancer. This will not only help improve the efficiency of early diagnosis of ovarian cancer but also provide new markers to support personalized treatment and prognostic assessment of ovarian cancer, providing more accurate diagnostic tools for clinical practice.

[0008] The technical solutions of the present invention are as follows:

[0009] In a first aspect, the present invention provides a specific metabolic marker combination for diagnosing ovarian cancer, which includes phosphatidylcholine 36:6, sphingomyelin d40:2 and sphingomyelin d41:2.

[0010] Furthermore, the specific metabolic marker combination consists of phosphatidylcholine 36:6, sphingomyelin d40:2 and sphingomyelin d41:2.

[0011] In the present invention, phosphatidylcholine 36:6 refers to a phosphatidylcholine molecule, and 36:6 means that there are 36 carbon atoms and 6 double bonds in the phosphatidylcholine molecule; taking sphingomyelin d40:2 as an example, it refers to a sphingomyelin molecule, and d40:2 means that the total number of carbon atoms of the long-chain base and amide fatty acid chain in the sphingomyelin molecule is 40, and the total number of double bonds is 2.

[0012] The specific metabolic marker combination of the present invention has high specificity and high sensitivity when used for ovarian cancer diagnosis, and is non-invasive, allowing patients to undergo testing conveniently and safely. It is suitable for large-scale screening and testing of the general population, and is conducive to early screening, early diagnosis and early treatment of ovarian cancer.

[0013] A second aspect of the present invention provides a method for screening the specific metabolic marker combination for diagnosing ovarian cancer according to the present invention, comprising the following steps:

[0014] Step A, extracting plasma samples from the healthy group and the ovarian cancer group respectively to obtain sample extracts;

[0015] Step B: using liquid chromatography-mass spectrometry to detect the aqueous phase and the organic phase in the sample extract to obtain detection data;

[0016] Step C: Processing the test data, then identifying metabolites, and then performing univariate receiver operating characteristic curve analysis (i.e., univariate ROC curve analysis) and orthogonal partial least squares discriminant analysis (i.e., OPLS-DA) to screen for differential metabolites between the healthy group and the ovarian cancer group, which are recorded as the first differential metabolite combination;

[0017] Step D: using the method for screening the first differential metabolite combination, differential metabolites between the healthy group and the endometrial cancer group are screened and recorded as the second differential metabolite combination;

[0018] Step E: using the method for screening the first differential metabolite combination, differential metabolites between the healthy group and the cervical cancer group are screened and recorded as the third differential metabolite combination;

[0019] Step F: Eliminate the differential metabolites of the first differential metabolite combination that overlap with the second differential metabolite combination and the third differential metabolite combination to obtain the specific metabolic marker combination for diagnosing ovarian cancer.

[0020] It should be noted that eliminating the differential metabolites of the first differential metabolite combination that overlap in the second differential metabolite combination and the third differential metabolite combination refers to eliminating the differential metabolites of the first differential metabolite combination that overlap with the second differential metabolite combination from the first differential metabolite combination, and eliminating the differential metabolites of the first differential metabolite combination that overlap with the third differential metabolite combination.

[0021] The present invention screens out differential metabolites of each group in the healthy group and the ovarian cancer group, the healthy group and the endometrial cancer group, and the healthy group and the cervical cancer group, respectively, and then eliminates the differential metabolites of the healthy group and the ovarian cancer group that overlap in the healthy group and the endometrial cancer group, and the healthy group and the cervical cancer group, thereby retaining the specific differential metabolites between the healthy group and the ovarian cancer group, and obtaining the specific metabolic marker combination for diagnosing ovarian cancer.

[0022] This is because ovarian cancer, endometrial cancer, and cervical cancer share similar metabolic disturbances. The differential metabolites identified between the healthy and ovarian groups also demonstrate strong diagnostic efficacy in distinguishing between the healthy and endometrial cancer groups, as well as between the healthy and cervical cancer groups. Therefore, to further improve the accuracy of ovarian cancer diagnosis, specific metabolic marker combinations were screened. This screening of specific metabolic marker combinations can help more accurately differentiate ovarian cancer patients and provide a basis for personalized treatment.

[0023] The present invention uses metabolomics technology to screen for a specific metabolic marker combination for ovarian cancer diagnosis. This specific metabolic marker combination has high sensitivity and high diagnostic capability for ovarian cancer. This specific metabolic marker combination also has high specificity for ovarian cancer diagnosis, but is not suitable for the diagnosis of endometrial and cervical cancers. Furthermore, the plasma sample acquisition method is non-invasive and convenient, and has broad application prospects in biomarker research.

[0024] Furthermore, the area under the univariate receiver operating characteristic curve (i.e., AUC value) greater than 0.77 and the VIP value greater than 1.5 were used as common screening conditions to screen out the first differential metabolite combination;

[0025] The second differential metabolite combination was screened using the area under the univariate receiver operating characteristic curve greater than 0.77 and the VIP value greater than 1.5 as common screening conditions;

[0026] The third differential metabolite combination was screened using the area under the univariate receiver operating characteristic curve greater than 0.77 and the VIP value greater than 1.5 as common screening conditions.

[0027] That is, based on univariate AUC values greater than 0.77 and VIP greater than 1.5, differential metabolites of each group were screened between the healthy group and the ovarian cancer group, the healthy group and the endometrial cancer group, and the healthy group and the cervical cancer group. Then, differential metabolites that overlapped between the healthy group and the ovarian cancer group and the endometrial cancer group, or between the healthy group and the cervical cancer group, were eliminated, thereby retaining the specific differential metabolites between the healthy group and the ovarian cancer group, thereby obtaining the specific metabolic marker combination for diagnosing ovarian cancer.

[0028] The third aspect of the present invention provides a use of the specific metabolic marker combination for diagnosing ovarian cancer according to the present invention in the preparation of a product for diagnosing ovarian cancer.

[0029] Furthermore, the product includes a detection reagent or a kit.

[0030] A fourth aspect of the present invention provides a product for diagnosing ovarian cancer, which comprises the specific metabolic marker combination for diagnosing ovarian cancer described in the present invention.

[0031] Furthermore, the product includes a detection reagent or a kit.

[0032] To address the current challenges in diagnosing and screening various gynecological tumors, this study uses metabolomics technology, combined with high-throughput analytical methods, to comprehensively screen for metabolic markers that show significant differences between healthy and ovarian cancer groups. The study also explores the potential of specific metabolic markers for early diagnosis. By measuring the metabolic levels of specific metabolic markers, early diagnosis of ovarian cancer is possible, and this method offers the advantages of being non-invasive, non-invasive, and highly accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Figure 3 ROC curves for differentiating the healthy group from the ovarian cancer group based on 39 differential metabolites of ovarian cancer in the validation set.

[0034] Figure 2 Figure 3 ROC curves for differentiating the healthy group from the endometrial cancer group based on 39 differential metabolites in ovarian cancer in the validation set.

[0035] Figure 3 ROC curve for differentiating the healthy group and cervical cancer group based on 39 differential metabolites of ovarian cancer in the validation group.

[0036] Figure 4 Figure 3 ROC curves for differentiating the healthy group from the ovarian cancer group based on three ovarian cancer-specific metabolic markers in the validation group.

[0037] Figure 5 Figure 3 ROC curves for differentiating the healthy group from the endometrial cancer group based on three ovarian cancer-specific metabolic markers in the validation group.

[0038] Figure 6 Figure 3 ROC curves for differentiating the healthy group from the cervical cancer group based on three ovarian cancer-specific metabolic markers in the validation group. DETAILED DESCRIPTION

[0039] The present invention provides a specific metabolic marker combination for diagnosing ovarian cancer and its application. To clarify and clarify the objectives, technical solutions, and effects of the present invention, the present invention is described in further detail below. It should be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. Specific embodiment:

[0041] Step 1: Test object and sample information

[0042] 1. Subjects

[0043] (1) Inclusion criteria:

[0044] Subjects must meet all of the following inclusion criteria to be eligible for this study: 1) female aged ≥18 years; 2) read and fully understood, signed the informed consent form, and were able to provide blood samples for metabolomics testing; 3) cancer group: confirmed by biopsy / postoperative pathology or clinically diagnosed as primary cervical cancer, endometrial cancer, and ovarian cancer through comprehensive evaluation by clinicians.

[0045] (2) Exclusion criteria:

[0046] Subjects who meet any of the following exclusion criteria are not eligible to participate in this study: 1) pregnant or breastfeeding; 2) emergency or rescue; 3) history of blood transfusion within 7 days before sampling; 4) those who have received organ transplantation or non-autologous (allogeneic) bone marrow or stem cell transplantation; 5) those with a history of malignant tumor within 5 years or any anti-tumor treatment before sampling; 6) those with multiple primary malignant tumors at the same time.

[0047] 2. Sample information

[0048] A total of 408 subjects, or 408 plasma samples, were recruited for this study, which came from plasma samples collected from two medical centers. The subjects included healthy people, cervical cancer patients, endometrial cancer patients, and ovarian cancer patients. The specific distribution is as follows: the modeling group had 291 plasma samples, including 99 plasma samples from healthy people, 59 plasma samples from cervical cancer patients, 69 plasma samples from endometrial cancer patients, and 64 plasma samples from ovarian cancer patients; the validation group had 117 plasma samples, including 24 plasma samples from healthy people, 39 plasma samples from cervical cancer patients, 23 plasma samples from endometrial cancer patients, and 31 plasma samples from ovarian cancer patients (Table 1). Plasma samples were collected in the early morning on an empty stomach, and all collected plasma samples were stored in a -80°C refrigerator.

[0049] Table 1. Sample information

[0050]

[0051] Step 2: Plasma sample pretreatment and metabolite detection

[0052] 1. Plasma sample pretreatment

[0053] After removing the plasma sample from a -80°C freezer, thaw it on ice and vortex mix it for 10 seconds. Subsequently, 100 μL of plasma was placed in 1000 μL of a pre-chilled mixture of methyl tert-butyl ether and methanol and vortex mix it to obtain a sample extract. Next, 500 μL of a mixture of methanol and water (methanol:water ratio, 3:1, by volume) was added to the sample extract, and the sample extract was centrifuged at 12,700 rpm for 5 minutes to separate the upper organic phase and the lower aqueous phase.

[0054] Aqueous phase: 400 μL of the aqueous phase from the lower layer was transferred to a centrifuge tube. 1100 μL of ice-cold methanol was added and vortexed to precipitate the protein. After protein precipitation, the tube was incubated at 4°C for 1 hour, then centrifuged at 12,700 rpm for 10 minutes at 4°C. 1000 μL of the supernatant was transferred to the corresponding aqueous phase centrifuge tube and dried in a Speed-Vac. The dried aqueous phase centrifuge tube was reconstituted with 200 μL of mass spectrometry-grade water and incubated at room temperature for 15 minutes. After incubation, the centrifuge tube was vortexed, ultrasonically treated for 5 minutes, and then centrifuged at 12,000 rpm for 5 minutes at room temperature. 180 μL of the supernatant was transferred from the centrifuge tube to a 2 mL glass vial. This was polar metabolites and was detected by LC-MS.

[0055] Organic phase: 350 μL of the organic phase from the upper layer was transferred to a centrifuge tube and dried using a Speed-Vac vacuum concentrator. The dried sample was reconstituted with 200 μL of a mixture of acetonitrile / isopropanol (v / v, 3:1) and incubated at room temperature for 15 minutes. After incubation, the tube was vortexed and ultrasonically treated (ultrasonication at room temperature) for 5 minutes. The tube was then centrifuged at 12,000 rpm for 5 minutes at room temperature. 180 μL of the supernatant was transferred from the centrifuge tube to a 2 mL glass vial for lipid metabolite analysis (LC-MS).

[0056] 2. High-resolution liquid chromatography-mass spectrometry (UHPLC-MS) detection

[0057] (1) Polar metabolite chromatographic parameters:

[0058] Polar metabolite analysis using Waters ACQUTTY An HSS T3 column (2.1*100 mm, 1.8 μm, where 2.1 mm is the inner diameter of the column, 100 mm is the length of the column, and 1.8 μm is the particle size of the filler) was used for small molecule separation. The column temperature was 40°C. The liquid chromatography and mass spectrometry were performed using ACQUITY UPLC, respectively. An I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific) were used. The mobile phase composition was as follows: mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an acetonitrile solution containing 0.1% formic acid, with a flow rate of 0.4 mL / min. The separation elution gradient was as follows: 0-1 min, 1% mobile phase B; 1-11 min, 1-40% mobile phase B; 11-13 min, 40-70% mobile phase B; 13-15 min, 70-99% mobile phase B; 15-18 min, 99% mobile phase B; 18-19 min, 1% mobile phase B, where % are all volume percentages. The sample injection volume was 3 μL, and the autosampler temperature was 10°C.

[0059] (2) Lipid metabolite chromatographic parameters:

[0060] Lipid metabolite analysis was performed using Waters ACQUTTY Small molecule separations were performed on a BEH C8 column (2.1 x 100 mm, 1.7 μm, where 2.1 mm represents the column inner diameter, 100 mm represents the column length, and 1.7 μm represents the particle size of the filler). The column temperature was 60°C. Liquid chromatography and mass spectrometry were performed using an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively. The mobile phase composition was as follows: mobile phase A was water, and mobile phase B was a mixture of acetonitrile / isopropanol (7 / 3, v / v), both containing 0.1% acetic acid and 1% ammonium acetate. The flow rate was 0.4 mL / min. The separation elution gradient was as follows: 0-1 min, 55% mobile phase B; 1-4 min, 55-75% mobile phase B; 4-12 min, 75-89% mobile phase B; 12-15 min, 89-100% mobile phase B; 15-19.5 min, 100% mobile phase B; 19.5 min-24 min, 55% mobile phase B; the injection volume was 2 μL, and the autosampler temperature was 10°C.

[0061] (3) LC-MS mass spectrometry parameters:

[0062] Full scan and data-dependent acquisition (DDA) were used to acquire primary (MS1) and secondary (MS2) mass spectrometric data for both polar and lipid metabolites. The full scan mass spectrometric range was 100–1500 Da. The secondary scan mode (Full MS / dd-MS2) scan ranges were 100–310, 300–710, and 700–1500 Da. The MS instrument used an Orbitrap high-resolution mass spectrometer (Thermo Fisher) equipped with an electrospray ionization (ESI) source, acquiring data in both positive and negative ionization modes. The specific parameters are as follows: automatic gain control (AGC) is 3E+6, Maximum IT is 200 ms, the first full scan (Fullscan) resolution is 70,000 FWHM (@200 m / z), in the second scan mode (Full MS / dd-MS2), the secondary mass spectrometer resolution is 17,500, the quadrupole window is 1.5 m / z, the automatic gain control (AGC) is 1E+5, the maximum ion injection time is 50 ms, the relative collision energy (HCD) is 30 eV; the ion source voltage is +3,500 V in positive ion mode and -3,500 V in negative ion mode, the nebulizer is 20 psi, the sheath gas temperature is 400°C, and the sheath gas flow rate is 10 L / min.

[0063] Step 3: Metabolomics data preprocessing and metabolite identification

[0064] 1. Metabolomics data preprocessing

[0065] Metabolomics data preprocessing steps include peak extraction, peak alignment, peak filtering, and missing value filling. First, the raw mass spectrometry data undergoes peak extraction, peak alignment, and peak filtering to reduce systematic data interference caused by instrument detection time fluctuations. These processes are performed separately for the polar metabolite detection platform and the lipid metabolite detection platform to avoid cross-platform interference. Retention times of the datasets are calibrated based on historical instrument detection data. After calibration, various filtering criteria are applied to remove the following interfering peaks: (i) isotopic peaks; (ii) fragmentation of the analyte caused by the ionization method; and (iii) redundant peaks, such as additional low-intensity adducts of the same analyte and redundant derivatives. This ensures the quality of the analyzable dataset. Characteristic peaks with >50% missing values across all samples are removed. Missing values are filled using the random forest chain equation (MICEForest). The processed data matrix serves as the raw data for statistical analysis and can be further processed through a series of data preprocessing steps, including normalization and standardization, to obtain the final mass spectrometry matrix data.

[0066] 2. Identification of metabolites

[0067] The raw data are analyzed by software to obtain the spectral information of the compound parent ion and secondary fragment ions, such as the mass-to-charge ratio (m / z) of the primary mass spectrum, the fragmentation of the secondary ion, and the retention time. The metabolites are then qualitatively identified by matching the spectral information of the primary and secondary metabolites in the database. Commonly used metabolite databases include HMDB (www.hmdb.ca), PubChem (https: / / pubchem.ncbi.nlm.nih.gov), and MassBank (http: / / www.

[0068] Metabolites were identified using the following databases: MassBank (https: / / www.massbank.jp), MassBank of North America (https: / / massbank.us), and the lipid databases Lipidmap (https: / / www.lipidmaps.org) and Lipidblast (https: / / fiehnlab.ucdavis.edu / projects / LipidBlast). Metabolites were ultimately verified based on retention time, MS1, and MS2 mass spectra of standards separated using the same column and mass spectrometry conditions. Metabolite identification criteria were retention time within 0.1 min and mass accuracy within 10 ppm.

[0069] Step 4: Develop a diagnostic method for distinguishing ovarian cancer based on metabolomics characteristics

[0070] 1. Screening of specific metabolite marker combinations

[0071] (1) Data standardization:

[0072] The pre-processed polar metabolomics data and lipid metabolomics data were respectively subjected to Log-transformation and NormAE normalization processing and then merged together to finally obtain mass spectrometry matrix data. Among them, Log-transformation aims to scale the original data to a specific interval to eliminate the order of magnitude differences between different samples; NormAE normalization processing aims to eliminate the batch effect between different samples, thereby obtaining standardized data for subsequent multivariate statistical analysis.

[0073] (2) Screening of differential metabolites:

[0074] To identify differential metabolites in the ovarian cancer group, multivariate statistical analysis was used to screen differential metabolites. Specifically, the univariate ROC curve AUC value and the VIP value in the orthogonal partial least squares discriminant analysis (OPLS-DA) model were combined to screen potential differential metabolites.

[0075] 1) First, univariate ROC analysis was used to evaluate the impact of individual metabolites on the performance of the ROC classification model. Specifically, univariate ROC curve analysis was performed on the healthy and ovarian cancer groups, and the area under the ROC curve (AUC) values of all individual metabolites were obtained. AUC values > 0.77 were used as a screening criterion to screen for differentially expressed metabolites between the healthy and ovarian cancer groups.

[0076] 2) Secondly, a multivariate statistical analysis was performed based on the OPLS-DA model to obtain the VIP value of all variables contributing to the model. The VIP value is used to measure the influence and explanatory power of each metabolite component content on the sample classification and discrimination, thereby assisting in the screening of differential metabolites. An OPLS-DA discriminant model was constructed between the healthy group and the ovarian cancer group, and the VIP value of each metabolite contributing to the model was obtained. Using VIP>1.5 as the screening condition, the differential metabolites between the two groups were screened out;

[0077] 3) Finally, the univariate AUC value > 0.77 and VIP > 1.5 were used as common screening conditions, and their intersection was taken to screen out a total of 39 differential metabolites between the healthy group and the ovarian cancer group.

[0078] Since different types of gynecological tumors may have the same differential metabolites, this may lead to the inability to accurately identify a single gynecological cancer. In order to further screen out specific metabolite markers in the ovarian cancer group, based on the univariate AUC value > 0.77 and VIP > 1.5, the differential metabolites of each group were screened in the healthy group and the endometrial cancer group, and the healthy group and the cervical cancer group, respectively. The 39 differential metabolites in the ovarian cancer group that overlapped in the healthy group and the endometrial cancer group, and the healthy group and the cervical cancer group were further eliminated, thereby retaining the specific differential metabolites between the healthy group and the ovarian cancer group (i.e., specific metabolic markers).

[0079] As shown in Table 2, three specific metabolic markers (phosphatidylcholine 36:6, sphingomyelin d40:2, and sphingomyelin d41:2) were finally screened out in the healthy group and the ovarian cancer group. Subsequently, a machine learning method was used to construct a diagnostic model based on specific metabolic markers to distinguish the healthy group from the ovarian cancer group.

[0080] Table 2. Combinations of three specific metabolic markers for distinguishing ovarian cancer

[0081]

[0082] 2. Build a diagnostic model between the healthy group and the ovarian cancer group

[0083] In order to evaluate the diagnostic effect of the selected specific metabolite marker combinations in distinguishing between the healthy group and the ovarian cancer group, a multivariate ROC curve analysis was performed on these specific metabolite marker combinations. Specifically, the sample data of the healthy group and the ovarian cancer group, endometrial cancer group, and cervical cancer group were randomly divided into a modeling group and a validation group data set, as shown in Table 1. In the modeling group data set, 3 / 4 was used as a training set (training) to build and train the machine learning classification model; the remaining 1 / 4 samples were used as a test set (test) to verify the discriminative ability of the trained model. The specific operation was as follows: using the modeling group data set, the support vector machine (SVM) algorithm was adopted, and 1000 random cycle iterations were performed. By calculating the average value of the model accuracy, a diagnostic model for distinguishing between healthy and ovarian cancer was constructed. The validation group data set was used as an unknown sample input into the diagnostic model for blind testing, thereby obtaining key indicators such as the area under the ROC curve (AUC), specificity, and sensitivity.

[0084] The ROC curve is a method for evaluating a model's balance between sensitivity and specificity, with sensitivity as the vertical axis and 1-specificity as the horizontal axis. The AUC value is a key indicator of model performance. When the AUC is greater than 0.5, the closer it is to 1, the better the model performance and the better the judgment. If it is less than 0.5, the model's performance is close to random guessing, with poor discrimination and accuracy. In addition to the area under the ROC curve (AUC), ROC classification model performance evaluation also includes sensitivity and specificity.

[0085] The sensitivity is:

[0086]

[0087] Specificity is:

[0088]

[0089] in:

[0090] TP (True Positive): The number of samples that are actually positive examples that are correctly predicted as positive examples;

[0091] TN (True Negative): The number of samples that are actually negative examples that are correctly predicted as negative examples;

[0092] FP (False Positive): The number of samples that are actually negative examples but are mistakenly predicted as positive examples;

[0093] FN (False Negative): The number of samples that are actually positive but are mistakenly predicted as negative.

[0094] 3. External Validation of the Diagnostic Model for Healthy Groups and Single Gynecological Tumor Groups

[0095] In order to evaluate the diagnostic effect of the combination of 39 differential metabolites in distinguishing between healthy and ovarian cancer. Multivariate ROC analysis was performed based on the above 39 differential metabolite combinations. In addition, the diagnostic performance of the 39 differential metabolites in distinguishing between healthy and endometrial cancer, and healthy and cervical cancer patients was further evaluated. Specifically, the diagnostic model was constructed using the modeling group data set, and the validation group data set was placed in the diagnostic model as an unknown sample to evaluate the validation effect of the model on the unknown data set other than the modeling group data set. The external validation results show (Table 3) that in the ROC diagnostic model of the healthy group and ovarian cancer group, AUC = 0.988 (specificity: 96.7%, sensitivity: 87.5%), see Figure 1 As shown; in the ROC diagnostic model of the healthy group and the endometrial group, AUC = 0.962 (specificity: 76.7%, sensitivity: 95.7%), see Figure 2 As shown; in the ROC diagnostic model of the healthy group and cervical cancer group, AUC = 0.953 (specificity: 90.3%, sensitivity: 95.8%), see Figure 3 These results suggest that, because ovarian cancer, endometrial cancer, and cervical cancer share similar metabolic disturbances, differential metabolites screened between the healthy and ovarian groups have a strong diagnostic effect in distinguishing between the healthy and endometrial cancer groups, as well as between the healthy and cervical cancer groups. Therefore, to further improve the accuracy of ovarian cancer diagnosis, screening for specific metabolic markers can help more accurately differentiate ovarian cancer patients and provide a basis for personalized treatment.

[0096] Table 3. Predictive performance of 39 differential metabolites in various gynecological tumors

[0097]

[0098] 4. Performance Evaluation of Specific Metabolite Biomarker Panels in Ovarian Cancer Models

[0099] To evaluate the predictive performance of specific metabolite markers in the ovarian cancer model, a multivariate receiver operating characteristic (ROC) curve analysis was performed on the three selected specific metabolite markers (Table 4): phosphatidylcholine 36:6, sphingomyelin d40:2, and sphingomyelin d41:2. This analysis evaluated their diagnostic performance in differentiating between healthy and ovarian cancer groups. Specifically, the modeling dataset was used to construct a diagnostic model, and the validation dataset was input into the diagnostic model for prediction.

[0100] Based on the samples of the external validation data set, each sample will output a probability value (Probability), which is the diagnostic threshold. Sensitivity and specificity can be calculated according to the corresponding formula. In the ROC multivariate curve analysis diagram, sensitivity (sensitivity) is the vertical axis and 1-specificity (1-specificity) is the horizontal axis. Different sensitivities and 1-specificities have corresponding points in the ROC curve. Each sample will output a diagnostic threshold. By connecting the diagnostic thresholds of each sample in the ROC curve, line segments with different gradients can be presented to draw the final ROC curve. In addition, the diagnostic threshold of the ROC model is selected based on the optimal sensitivity and specificity.

[0101] The results showed that the diagnostic threshold of the model was 0.4803. The confusion matrix results in Table 5 showed that among the 31 ovarian cancer patients, 27 were classified as ovarian cancer group and 4 were misclassified as healthy group; among the 24 healthy people, 20 were classified as healthy group and 4 were misclassified as ovarian cancer group. The external validation ROC analysis results of the specific metabolic markers in the diagnostic model of the healthy group and ovarian cancer group showed ( Figure 4 ), AUC = 0.93 (specificity: 87.1%, sensitivity: 83.3%), indicating that the three specific metabolic markers screened out have high diagnostic ability in the diagnosis of ovarian cancer.

[0102] Table 4. Predictive performance of ovarian cancer-specific metabolic markers in various gynecological tumors

[0103]

[0104] Table 5. Confusion matrix of the diagnostic model for differentiating the healthy group from the ovarian cancer group using specific metabolic markers

[0105] Ovarian cancer group Healthy Group Ovarian cancer group, N=31 27(TP) 4(FN) Healthy group, N=24 4(FP) 20(TN)

[0106] To further validate the specific metabolic markers in the ovarian cancer patient model, a multivariate ROC curve analysis was performed in the non-ovarian cancer patient model. Figure 5 ), in the ROC curve diagnostic model for endometrial cancer patients, AUC = 0.55 (specificity: 63.3%; sensitivity: 52.2%); in the ROC curve diagnostic model for cervical cancer patients ( Figure 6 ), AUC = 0.68 (specificity: 64.5%; sensitivity: 50%). This indicates that the three specific metabolic markers screened out have high specificity in the diagnosis of ovarian cancer patients, but are not applicable to the diagnosis of endometrial cancer and cervical cancer.

[0107] In summary, the specific metabolite markers screened for the healthy and ovarian cancer groups performed well in the ovarian cancer model, demonstrating high specificity. Furthermore, the ovarian cancer-specific metabolite markers were not applicable to the diagnosis of the other two gynecological cancers, indicating that the selected metabolite markers between the healthy and ovarian cancer groups exhibited strong specificity.

[0108] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, 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 specific metabolic marker combination for diagnosing ovarian cancer, characterized in that: Includes phosphatidylcholine 36:6, sphingomyelin d40:2 and sphingomyelin d41:

2.

2. The specific metabolic marker combination for diagnosing ovarian cancer according to claim 1, characterized in that: The specific metabolic marker combination consists of phosphatidylcholine 36:6, sphingomyelin d40:2 and sphingomyelin d41:

2.

3. A method for screening a specific metabolic marker combination for diagnosing ovarian cancer according to any one of claims 1 to 2, characterized in that: The steps include: Step A, extracting plasma samples from the healthy group and the ovarian cancer group respectively to obtain sample extracts; Step B, using liquid chromatography-mass spectrometry to detect the aqueous phase and the organic phase in the sample extract to obtain detection data; Step C, processing the test data, then identifying metabolites, and then performing univariate receiver operating characteristic curve analysis and orthogonal partial least squares discriminant analysis to screen out differential metabolites between the healthy group and the ovarian cancer group, which are recorded as the first differential metabolite combination; Step D: using the method for screening the first differential metabolite combination, differential metabolites between the healthy group and the endometrial cancer group are screened and recorded as the second differential metabolite combination; Step E: using the method for screening the first differential metabolite combination, differential metabolites between the healthy group and the cervical cancer group are screened and recorded as the third differential metabolite combination; Step F: Eliminate the differential metabolites of the first differential metabolite combination that overlap with the second differential metabolite combination and the third differential metabolite combination to obtain the specific metabolic marker combination for diagnosing ovarian cancer.

4. The screening method for a specific metabolic marker combination for diagnosing ovarian cancer according to claim 3, characterized in that: The first differential metabolite combination was screened using the area under the univariate receiver operating characteristic curve greater than 0.77 and the VIP value greater than 1.5 as common screening conditions; The second differential metabolite combination was screened using the area under the univariate receiver operating characteristic curve greater than 0.77 and the VIP value greater than 1.5 as common screening conditions; The third differential metabolite combination was screened using the area under the univariate receiver operating characteristic curve greater than 0.77 and the VIP value greater than 1.5 as common screening conditions.

5. Use of the specific metabolic marker combination for diagnosing ovarian cancer according to any one of claims 1 to 2 in the preparation of a product for diagnosing ovarian cancer.

6. The use according to claim 5, characterized in that The products include detection reagents or kits.

7. A product for diagnosing ovarian cancer, characterized in that: The method comprises the specific metabolic marker combination for diagnosing ovarian cancer as described in any one of claims 1-2.

8. The product for diagnosing ovarian cancer according to claim 7, characterized in that: The products include detection reagents or kits.

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