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

Abstract

The invention provides a metabolic marker composition for distinguishing health from breast cancer. Based on metabonomics data of blood plasma, a blood plasma metabolism marker combination for breast cancer diagnosis is screened, and a breast cancer diagnosis model with relatively high sensitivity and specificity is constructed in combination with a machine learning algorithm. The method has high sensitivity and specificity, samples are convenient to obtain, non-invasive performance is achieved, accurate screening of the breast cancer can be achieved, important help is provided for prevention of the breast cancer and reduction of the morbidity, and the method is suitable for large-scale population screening and long-term tracking in areas with shortage of medical resources.

Description

Technical Field

[0001] This invention relates to the field of metabolic biomarker analysis and application, specifically to a combination of metabolic biomarkers for distinguishing between healthy individuals and breast cancer, and their applications. Background Technology

[0002] Breast cancer is a malignant tumor that primarily affects women, threatening the health of a large number of women. Statistics show that among cancers affecting women worldwide, breast cancer has a higher incidence and mortality rate than lung cancer, currently ranking first. In 2020, approximately 2.3 million women were diagnosed with breast cancer, and about 685,000 died from it. Many factors, including congenital factors, diet, environment, work, stage of development, and estrogen-related medications, can contribute to the development of breast cancer.

[0003] Currently, early diagnosis of breast cancer commonly relies on imaging examinations (clinical breast physical examination, ultrasound, mammography, MRI, etc.) and clinical screening using tumor markers (CEA, CA153, VEGF, TSGF, etc.). The former is complex and invasive, while the latter requires multiple tests and has low specificity, neither of which adequately meets clinical needs. Despite advancements in surgery and chemotherapy, the efficacy of anti-tumor drugs remains limited, resulting in poor prognoses for breast cancer patients, especially with widespread late-stage recurrence and metastasis. Therefore, identifying early metabolic biomarkers for breast cancer and enabling safe and sensitive early diagnosis is crucial for the diagnosis and treatment of breast cancer.

[0004] As a rising star following genomics, transcriptomics, and proteomics, metabolomics focuses on studying the metabolic changes of endogenous small molecules under different pathophysiological or gene mutation conditions. Endogenous small molecules are downstream products of genes and proteins, reflecting the influence of upstream genes and external factors on bodily functions in real time at the molecular biological level. Metabolomics employs modern instrumental analytical techniques characterized by high sensitivity and high throughput to dynamically analyze endogenous small molecules in the body. With the development of breast cancer, abnormal changes occur in small molecule metabolites related to the metabolism of amino acids, carbohydrates, and lipids in patients.

[0005] Metabolomics, by analyzing changes in small molecule metabolites (such as amino acids, lipids, and carbohydrates) in organisms, reveals abnormalities in disease-related metabolic pathways, demonstrating significant potential in the early diagnosis, subtyping, prognostic assessment, and exploration of therapeutic targets for breast cancer. Therefore, this patent, based on metabolomics data from plasma samples, constructs a highly sensitive and specific breast cancer diagnostic model, providing effective assistance for breast cancer diagnosis and enabling early screening, early detection, and early treatment.

[0006] The technical problem to be solved by this invention is to overcome the defects and shortcomings of the prior art and provide a set of plasma metabolic markers for breast cancer screening and diagnosis. By detecting the set of metabolic markers in plasma samples, it can be determined whether a patient has breast cancer. This method has high sensitivity and specificity, is non-invasive, and can achieve accurate screening of breast cancer, providing important help for the prevention of breast cancer and reducing its incidence. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing breast cancer detection and screening methods by providing a combination of metabolic biomarkers for distinguishing between healthy individuals and breast cancer, and their applications.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] This invention discloses a metabolic biomarker composition for distinguishing between healthy individuals and breast cancer, the composition comprising: indole-3-lactic acid, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0010] Preferably, the composition comprises: phosphatidylethanolamine 36:3p, indole-3-lactic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0011] Preferably, the composition comprises: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0012] Preferably, the composition comprises: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

[0013] Preferably, the composition comprises: acylcarnitine 11:1, phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, guanidinoacetic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, L-threonyl-L-alanine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

[0014] Preferably, the composition comprises the following metabolic markers: indole-3-lactic acid, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0015] Preferably, the composition comprises the following metabolic markers: phosphatidylethanolamine 36:3p, indole-3-lactic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0016] Preferably, the composition comprises the following metabolic markers: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0017] Preferably, the composition comprises the following metabolic markers: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

[0018] Preferably, the composition comprises the following metabolic markers: acylcarnitine 11:1, phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, guanidinoacetic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, L-threonyl-L-alanine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

[0019] This invention discloses a combination of metabolic biomarkers for distinguishing between healthy individuals and breast cancer, the combination comprising: indole-3-lactic acid, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0020] Preferably, the combination comprises: phosphatidylethanolamine 36:3p, indole-3-lactic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0021] Preferably, the combination comprises: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0022] Preferably, the combination comprises: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

[0023] Preferably, the combination comprises: acylcarnitine 11:1, phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, guanidinoacetic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, L-threonyl-L-alanine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

[0024] Preferably, the combination consists of the following metabolic markers: indole-3-lactic acid, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0025] Preferably, the combination consists of the following metabolic markers: phosphatidylethanolamine 36:3p, indole-3-lactic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0026] Preferably, the combination consists of the following metabolic markers: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

[0027] Preferably, the composition comprises the following metabolic markers: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

[0028] Preferably, the combination consists of the following metabolic markers: acylcarnitine 11:1, phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, guanidinoacetic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, L-threonyl-L-alanine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

[0029] This invention discloses the use of the described composition in the preparation of a kit for distinguishing between healthy and breast cancer.

[0030] This invention discloses the use of the described composition in the preparation of a reagent for distinguishing between healthy and breast cancer.

[0031] This invention discloses the use of the aforementioned combination in the preparation of a reagent kit for distinguishing between healthy and breast cancer.

[0032] This invention discloses the use of the aforementioned combination in the preparation of a reagent that distinguishes between healthy and breast cancer.

[0033] Preferably, the samples used in the differentiation process are selected from serum, plasma, or tissue fluid.

[0034] This invention discloses a kit for distinguishing between healthy individuals and breast cancer, the kit comprising the aforementioned metabolic biomarker composition.

[0035] Preferably, the kit also includes quality control products and standards.

[0036] Compared with existing technologies, this invention constructs a breast cancer diagnostic model with high sensitivity and specificity based on plasma metabolomics data. This method is convenient, economical, easy to obtain samples, non-invasive, and has higher subject compliance, making it more suitable for large-scale population screening and long-term follow-up in areas with limited medical resources. Attached Figure Description

[0037] Figure 1 In the modeling group, multivariate ROC curve analysis was performed on 15 key biomarkers that distinguish between HC and BC.

[0038] Figure 2 In the validation group, multivariate ROC curve analysis was performed on 15 key biomarkers that distinguish between HC and BC. Detailed Implementation

[0039] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0040] Example 1: Subject Information and Sample Grouping

[0041] 1. Subject Information

[0042] 1) Sample inclusion criteria:

[0043] Participants must meet all of the following inclusion criteria to be eligible to participate in this study:

[0044] (1) Females aged ≥18 years;

[0045] (2) Read and fully understand the information, sign the informed consent form, and be able to provide a plasma sample for metabolomics testing;

[0046] (3) Breast cancer group: Patients diagnosed with primary malignant breast tumors by biopsy / postoperative pathology or by comprehensive clinical evaluation by clinicians.

[0047] 2) Sample exclusion criteria:

[0048] Subjects who meet any of the following exclusion criteria are ineligible to participate in this study:

[0049] (1) During pregnancy or lactation;

[0050] (2) Emergency room visit or resuscitation required;

[0051] (3) History of blood transfusion within 7 days prior to sampling;

[0052] (4) People who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants;

[0053] (5) History of malignant tumor within 5 years or any anti-tumor treatment before sampling;

[0054] (6) Simultaneous co-occurrence of multiple primary malignant tumors.

[0055] 3) Subject information:

[0056] This study collected plasma samples from 263 participants across two medical centers, including 127 healthy (HC) participants and 136 breast cancer (BC) participants. Specifically, the plasma samples used for the modeling group consisted of 96 healthy (HC) participants and 102 breast cancer (BC) participants; the plasma samples used for the validation group consisted of 31 healthy (HC) participants and 34 breast cancer (BC) participants (Table 1).

[0057] Table 1. Subject Information

[0058] Health (HC) Breast cancer (BC) Number of people in the modeling team 96 102 Number of people in the verification group 31 34 total 127 136

[0059] Example 2: Detection of plasma metabolites

[0060] 1. Reagents:

[0061] Methanol, acetonitrile, water, acetic acid, and isopropanol of mass spectrometry grade purity, and formic acid, ammonium acetate, and methyl tert-butyl ether of chromatographic (HPLC) grade purity were purchased from Sigma-Aldrich, USA.

[0062] 2. Sample preparation:

[0063] Take 100 μL of plasma and place it in 1000 μL of pre-cooled (methyl tert-butyl ether: methanol, volume ratio 3:1) solution. Vortex to mix the extracted blood sample and obtain the sample extract. Add 500 μL of (methanol: water, volume ratio 3:1) solution to the sample extract, sonicate, let stand, vortex and centrifuge to separate the layers. The upper layer is the organic phase and the lower layer is the aqueous phase.

[0064] -Organic phase: After the sample is separated into layers, take 500 μL of the upper organic phase into a centrifuge tube, dry it, add 200 μL of (acetonitrile:isopropanol, volume ratio 3:1), and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm); take 180 µL of the supernatant from the centrifuge tube into a 2 mL glass vial, which is the organic phase test solution, and perform LC-MS detection.

[0065] –Aqueous phase: After sample separation, take 400 μL of the lower aqueous phase into a centrifuge tube and add 1100 μL of ice-cold methanol to precipitate proteins. After protein precipitation, centrifuge the tube and transfer 1000 μL of the supernatant to a new centrifuge tube, then dry it 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, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm). Take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass vial as the aqueous phase test solution, and perform LC-MS analysis.

[0066] 3. Detection of small molecule metabolites:

[0067] For small molecule separation, a Waters ACQUTTY UPLC® BEH C8 1.7µm 2.1*100mm column was used for the organic phase, and a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column was used for the aqueous phase. The liquid chromatography and mass spectrometry systems used were the ACQUITY UPLC I-Class liquid chromatography system (Waters) and the Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0068] The mobile phase parameters are as follows:

[0069] Organic phase test solution mobile phase parameters - Mobile phase A is an aqueous solution containing 0.1% acetic acid and 10 mmol ammonium acetate; Mobile phase B is an acetonitrile-isopropanol (7:3 v / v) solution containing 0.1% acetic acid and 10 mmol ammonium acetate. 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.

[0070] Aqueous test liquid mobile phase parameters –

[0071] Mobile phase A is an aqueous solution containing 0.1% formic acid; mobile phase B is an acetonitrile solution containing 0.1% formic acid. The separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, and 13-18 minutes is 99% mobile phase B.

[0072] The mass spectrometry parameters are as follows:

[0073] Mass spectrometry data were acquired using Full MS and Full MS / dd-MS2 (each with both positive and negative modes). The parameters used by QExactive were as follows: Full MS mode had a resolution of 70,000 m / z, a scan range of 100-1500 m / z, an AGC of 3E+6, and a maximum IT of 200 ms; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer was 17,500 m / z, the quadrupole window was 1.5 m / z, the AGC was 1E+5, the maximum ion implantation time was 50 ms, and the HCD relative collision energy was 30%.

[0074] Example 3: Metabolomics Data Processing

[0075] 1. Metabolomics data preprocessing and metabolite identification

[0076] 1) Metabolomics data processing:

[0077] (1) Extract peaks from the RAW format file of the mass spectrometer and convert it into a FeatureXML format file to reduce the dimensionality of the original mass spectrometry data and improve the signal-to-noise ratio;

[0078] (2) 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;

[0079] (3) Match and filter the isotope peaks in the data matrix obtained in step (2), and then replace the abnormal data (0, negative values, background noise, etc.) with missing values;

[0080] (4) Remove the feature peaks with a detection rate of <80% from all the feature peaks obtained in step (3), fill the median value of the feature peaks with a detection rate of >80%, and add 5% random noise (following a standard normal distribution).

[0081] (5) 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 normalization to remove systematic errors such as batch effects.

[0082] 2) Identification of metabolites:

[0083] After analyzing the raw data using software, the spectral information of the primary precursor ion (MS1) and secondary fragment ion (MS2) of the compound is obtained. This information, such as the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragment ion data, is matched with the spectral information of primary and secondary metabolites in public databases 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), and the Mass Spectrometry Database (www.massbank.jp). Metabolites identified based on these databases are then finally validated using retention times, MS1, and MS2 mass spectrometry data obtained from separation of standards under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are a retention time difference within 0.1 min and a theoretical and measured molecular weight difference of less than 10 ppm.

[0084] 2. Data Analysis

[0085] 1) Screening for metabolic biomarkers to distinguish between healthy individuals and breast cancer

[0086] First, LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis was performed on the data from the modeling group, and a total of 15 differential metabolites were screened out (Table 2) as important metabolic markers to distinguish between healthy individuals and breast cancer.

[0087] Table 2. 15 Important Metabolic Markers that Differentiate Between Healthy Individuals and Breast Cancer

[0088] Logo - English Logo - Chinese 1 AcCa 11:1 Acylcarnitine 11:1 2 PE 36:3p Phosphatidylethanolamine 36:3p 3 Homoserine lactone Homoserine lactone 4 5-Aminolevulinic acid 5-Aminolevulinic acid 5 Glycocyamine Guanidinoacetic acid 6 Indole-3-lactic acid Indole-3-lactic acid 7 Glutaminic acid glutamic acid 8 myo-Inositol 2-phosphate Inositol 2-phosphate 9 Nicotinamide Niacinamide 10 LysoPE 16:0 Lysophosphatidylethanolamine 16:0 11 Octanoylcarnitine Capryloylcarnitine 12 L-Threonyl-L-alanine L-Threonyl-L-alanine 13 Dehydroepiandrosterone sulfate Dehydroepiandrosterone sulfate 14 D-Ribulose D-ribulose 15 Sphinganine 1-phosphate Dihydrosphingosine 1-phosphate

[0089] 2) Construction of a diagnostic model to distinguish between healthy individuals and breast cancer

[0090] To validate the diagnostic efficacy of the 15 selected metabolic biomarkers in distinguishing between healthy individuals and breast cancer, multivariate ROC curve analysis was performed on these 15 biomarkers in the modeling group. Specifically, three-quarters of the sample data from the HC and BC groups in the modeling group were randomly used as the training set, and one-quarter as the test set. A support vector machine (SVM) was used for randomized iterations of 1000 times, and the diagnostic model for distinguishing between healthy individuals and breast cancer was constructed by statistically analyzing the average accuracy of the final model.

[0091] ROC curves are a method for studying the relationship between model sensitivity and specificity. Sensitivity is plotted on the ordinate, and 1-specificity on the x-axis. The evaluation criterion is the area under the curve (AUC). An AUC greater than 0.5, and closer to 1, indicates better model performance and diagnostic effectiveness. An AUC less than 0.5 indicates poor model accuracy. ROC classification prediction models, in addition to common parameters such as the receiver operating characteristic (ROC) curve and AUC, also include sensitivity and specificity.

[0092] Sensitivity is:

[0093]

[0094] Specificity is:

[0095]

[0096] in,

[0097] TP (True Positive): The number of samples that are actually positive but were correctly predicted as positive.

[0098] TN (True Negative): The number of samples that are actually negative but were correctly predicted as negative.

[0099] FP (False Positive): The number of samples that are actually negative but are incorrectly predicted as positive.

[0100] FN (False Negative): The number of samples that are actually positive but are incorrectly predicted as negative.

[0101] The results are as follows Figure 1 As shown, the AUC = 0.978 (sensitivity = 0.885, specificity = 0.958), indicating that the constructed diagnostic model has high diagnostic efficacy.

[0102] In addition, 12 metabolic markers were analyzed: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamate, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, caprylcarnitine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate combination; 9 metabolic markers: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamate, inositol 2-phosphate, nicotinamide, caprylcarnitine, and dehydroepiandrosterone sulfate combination; 6 metabolic markers: phosphatidylethanolamine 36:3p, indole-3-lactic acid, inositol 2-phosphate, nicotinamide, caprylcarnitine, and dehydroepiandrosterone sulfate combination; and 3 metabolic markers: indole-3-lactic acid, caprylcarnitine, and dehydroepiandrosterone sulfate combination. Diagnostic models for distinguishing between healthy individuals and breast cancer were constructed. Results showed that the diagnostic model constructed with a combination of 12 metabolic markers had an AUC of 0.965 (sensitivity = 0.92, specificity = 0.875), the model constructed with a combination of 9 metabolic markers had an AUC of 0.975 (sensitivity = 0.92, specificity = 0.917), the model constructed with a combination of 6 metabolic markers had an AUC of 0.942 (sensitivity = 0.885, specificity = 0.875), and the model constructed with a combination of 3 metabolic markers had an AUC of 0.92 (sensitivity = 0.92, specificity = 0.875). These results indicate that the diagnostic models constructed using combinations of 12, 9, 6, and 3 metabolic markers, respectively, all possess high diagnostic efficacy and clinical diagnostic significance.

[0103] 3) Validation of diagnostic models used to distinguish between healthy individuals and breast cancer

[0104] To further validate the effectiveness of the diagnostic model for distinguishing between healthy and breast cancer, built based on the modeling group data, validation group data was used to validate the model. Specifically, multivariate ROC curve analysis was performed to evaluate the independent validation performance of the diagnostic model on unknown datasets outside the modeling group dataset. After the validation group samples were placed into the diagnostic model constructed by the modeling group, the probability value was output based on the detection data of 15 important metabolic markers distinguishing between healthy and breast cancer for each sample. Using the probability value of each sample as the diagnostic threshold, a confusion matrix (including true positive, true negative, false positive, and false negative) was obtained. Sensitivity and specificity can be calculated using formulas, and a point can be marked on the ROC analysis graph 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 graph. Connecting these points will produce an ROC curve. Figure 2Among them, the point with the best sensitivity and specificity was selected, and the diagnostic threshold at this point was 0.644.

[0105] As shown in Table 3, the confusion matrix results indicate that, based on the 15 metabolic biomarkers, the diagnostic model, with a diagnostic threshold of 0.644, resulted in 31 out of 34 breast cancer patients being diagnosed with breast cancer and 3 being misdiagnosed as healthy individuals; among the 31 healthy subjects, 29 were correctly diagnosed and 2 were misdiagnosed with breast cancer. The ROC analysis results of the diagnostic model in the validation group are as follows: Figure 2 As shown, sensitivity and specificity were calculated based on the confusion matrix results, with an AUC of 0.981 (sensitivity = 0.912, specificity = 0.935). These results indicate that the constructed diagnostic model for distinguishing between healthy individuals and breast cancer also demonstrated good diagnostic performance in the validation group.

[0106] Table 3. Confusion matrix of diagnostic models used to distinguish between healthy individuals and breast cancer

[0107] Breast cancer healthy subjects 34 cases of breast cancer 31 (TP) 3 (FN) 31 healthy subjects 2 (FP) 29 (TN)

[0108] In addition, 12 metabolic markers were analyzed: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamate, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate combination; 9 metabolic markers: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamate, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate combination; 6 metabolic markers: phosphatidylethanolamine 36:3p, indole-3-lactic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate combination; and 3 metabolic markers: indole... The diagnostic model based on the combination of lactate, capryloylcarnitine, and dehydroepiandrosterone sulfate was validated in the validation group. The results showed that the diagnostic model constructed with 12 metabolic markers had an AUC of 0.986 (sensitivity = 0.941, specificity = 0.871), the model constructed with 9 metabolic markers had an AUC of 0.98 (sensitivity = 0.882, specificity = 0.871), the model constructed with 6 metabolic markers had an AUC of 0.972 (sensitivity = 0.882, specificity = 0.903), and the model constructed with 3 metabolic markers had an AUC of 0.939 (sensitivity = 0.941, specificity = 0.839). These results indicate that the constructed diagnostic model for distinguishing between healthy individuals and breast cancer also demonstrated good diagnostic efficacy in the validation group.

[0109] The present invention has been illustrated through the above embodiments, but the present invention is not limited to the above process steps, that is, it does not mean that the present invention must rely on the above process steps to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions of the raw materials used in the present invention, additions of auxiliary components, and selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A metabolic biomarker composition for distinguishing between healthy individuals and breast cancer, characterized in that, The composition comprises: indole-3-lactic acid, capryloylcarnitine, and dehydroepiandrosterone sulfate.

2. The composition according to claim 1, characterized in that, The composition comprises: phosphatidylethanolamine 36:3p, indole-3-lactic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

3. The composition according to claim 2, characterized in that, The composition comprises: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, capryloylcarnitine, and dehydroepiandrosterone sulfate.

4. The composition according to claim 3, characterized in that, The composition comprises: phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

5. The composition according to claim 4, characterized in that, The composition comprises: acylcarnitine 11:1, phosphatidylethanolamine 36:3p, homoserine lactone, 5-aminolevulinic acid, guanidinoacetic acid, indole-3-lactic acid, glutamic acid, inositol 2-phosphate, nicotinamide, lysophosphatidylethanolamine 16:0, capryloylcarnitine, L-threonyl-L-alanine, dehydroepiandrosterone sulfate, D-ribulose, and dihydrosphingosine 1-phosphate.

6. Use of the composition according to any one of claims 1-5 in the preparation of a kit for distinguishing between healthy and breast cancer.

7. Use of the composition according to any one of claims 1-5 in the preparation of a reagent for distinguishing between healthy and breast cancer.

8. The use according to any one of claims 6-7, characterized in that, The samples used in the differentiation process are selected from serum, plasma, or tissue fluid.

9. A reagent kit for distinguishing between healthy breast cancer and breast cancer, characterized in that, The kit comprises the metabolic biomarker composition according to any one of claims 1-5.

10. The reagent kit according to claim 9, characterized in that, The kit also includes quality control materials and standards.

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