Anion saliva metabolite marker related to brain glioma, product and application of anion saliva metabolite marker

Abstract

The invention discloses a negative ion saliva metabolite marker related to brain glioma, a product and application of the negative ion saliva metabolite marker and relates to the field of biological medicine. The negative ion saliva metabolite marker is prepared from one or more of heptadecanoic acid, phosphatidylcholine 36: 5, 11-dehydrothromboxane B2 and flavone diacetate. The invention provides a kit, which is used for detecting negative ion saliva metabolite markers related to brain glioma and comprises a specific detection reagent. In addition, the invention also provides a computer program product which is specially used for evaluating brain glioma. The products have good feasibility and accuracy, the risk of brain glioma can be effectively evaluated, and a brand new powerful tool is provided for clinical diagnosis.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to a negative ion saliva metabolite marker related to brain glioma, a product and an application thereof. Background Art

[0002] Currently, the methods used to diagnose brain gliomas are mainly divided into the following categories: computed tomography (CT), magnetic resonance imaging (MRI) and molecular typing diagnosis.

[0003] Computed tomography is a preliminary screening method for gliomas, which can quickly detect intracranial space-occupying lesions, but it has problems such as low resolution of soft tissue and easy missed diagnosis (misdiagnosis); magnetic resonance imaging (MRI) is the core imaging tool for glioma diagnosis, which can provide anatomical, functional and molecular characteristics of the tumor and can be used to assist in preoperative planning and prognosis assessment, but it has high examination costs and needs to be used in conjunction with molecular typing diagnosis; molecular typing is the "gold standard" for diagnosing gliomas, but it requires surgery (or biopsy) to obtain pathological tissue, which carries the risk of trauma and may delay diagnostic decisions.

[0004] Mass spectrometry (MS) is a highly sensitive analytical technique that identifies the structure and composition of compounds by measuring the mass-to-charge ratio (m / z) of ions. Positive ion mode (Positive Ion Mode) and negative ion mode (Negative Ion Mode) are the two core and optional detection methods in mass spectrometry (MS). The detection mode (positive ion mode or negative ion mode) selected by the detector directly affects the sensitivity and accuracy of the analysis results.

[0005] When performing mass spectrometry analysis of metabolites, positive ion metabolites, due to their positive charge, can be detected using positive ion mode. Positive ion mode is suitable for the analysis and detection of alkaline or neutral-alkaline metabolites, such as amino acids (e.g., leucine, lysine), amines (e.g., choline, histamine), and some lipids (e.g., phosphatidylcholine, triglycerides). Conversely, negative ion metabolites, due to their negative charge, require detection using negative ion mode, which is more suitable for the analysis and detection of acidic or neutral-alkaline metabolites, such as organic acids (e.g., citric acid, succinic acid), phenols (e.g., caffeic acid, ferulic acid), and some lipids (e.g., fatty acids, phosphatidylserine).

[0006] Based on this, the research on brain gliomas, screening out related acidic or neutral-acidic metabolites through negative ion mode, is expected to provide a new idea and approach for the diagnosis of brain gliomas. Summary of the Invention

[0007] In response to the above technical problems, the present invention provides a negative ion saliva metabolite marker related to glioma, a product and an application thereof, so as to screen out acidic or neutral-acidic metabolites related to glioma, thereby providing a new idea and approach for the diagnosis of glioma.

[0008] The technical solutions provided by the present invention are as follows: In a first aspect, a negative ion salivary metabolite marker associated with brain glioma is provided, characterized in that the negative ion salivary metabolite marker includes 11-dehydrothromboxane B2 and / or acetic acid flavonoid diacetate.

[0009] Furthermore, the negative ion salivary metabolite marker further includes heptadecanoic acid and / or phosphatidylcholine 36:5 (Pc 36:5).

[0010] Furthermore, the negative ion saliva metabolite markers include heptadecanoic acid, phosphatidylcholine 36:5 (Pc 36:5), 11-dehydrothromboxane B2, and acetic acid flavonoid diacetate.

[0011] In a second aspect, a reagent for detecting negative ion salivary metabolite markers is provided for use in preparing a product for diagnosing or screening brain gliomas, wherein the negative ion salivary metabolite markers include any one or more of heptadecanoic acid, phosphatidylcholine 36:5 (Pc 36:5), 11-dehydrothromboxane B2, and acetic acid flavonoid diacetate.

[0012] In a third aspect, a kit is provided, comprising a detection reagent for detecting the negative ion salivary metabolite marker described in the first aspect.

[0013] In a fourth aspect, the present invention provides the use of the kit described in the third aspect in preparing a product for detecting brain glioma.

[0014] In a fifth aspect, use of the detection reagent in the kit described in the third aspect in preparing a kit for diagnosing brain glioma is provided.

[0015] In a sixth aspect, a computer program product related to glioma is provided, wherein the computer program product is used to diagnose whether a subject has a risk of glioma, comprising the following steps: Obtaining the expression level of a single negative ion salivary metabolite marker in the test subject; wherein the single negative ion salivary metabolite marker includes any one of heptadecanoic acid, phosphatidylcholine 36:5 (Pc 36:5), 11-dehydrothromboxane B2, and acetindiacetate; Calculate the logarithm y of the odds of the subject to be tested according to the binary logistic regression equation; Calculate the probability Z of the subject being healthy based on y, Z = exp(y) / {1+exp(y)}; where exp(y) is the exponential function of y; Based on the comparison of the probability Z with the reference value, it is diagnosed or predicted whether the subject to be tested suffers from a brain glioma or has a risk of suffering from a brain glioma.

[0016] In one possible implementation, the binary logistic regression equation is formulated as: y=A+B1×x1+B2×x2+B3×x3+B4×x4; Among them, A is the intercept term, B1-B6 are the regression coefficients of the independent variables; x1 to x4 are the expression levels of 11-dehydrothromboxane b2, acetic acid diacetate, heptadecanoic acid, and phosphatidylcholine 36:5 (Pc 36:5), respectively.

[0017] Furthermore, the A is -4.586×10 -1 , B1 is 3.822×10 -8 , B2 is 1.392×10 -7 , B3 is -1.050×10 -7 , B4 is -1.935×10 -7 .

[0018] The beneficial effects of the present invention are as follows: 1. The expression levels of the metabolites of heptadecanoic acid and phosphatidylcholine 36:5 (Pc 36:5) of the present invention were significantly higher in the glioma disease group than in the healthy group; the expression levels of the metabolites of 11-dehydrothromboxane B2 and acetic acid willow flavonoid diacetate were significantly lower in the glioma disease group than in the healthy group.

[0019] ROC curve analysis showed that the four markers described above had high specificity and sensitivity as detection variables. Therefore, these four metabolites can be used as detection markers for the prediction and diagnosis of patients with glioma. Using these four metabolites as detection markers is completely non-invasive and highly accurate.

[0020] 2. The kit of the present invention can use these four metabolites as detection markers to predict or diagnose brain glioma, which is completely non-invasive and highly accurate.

[0021] 3. The present invention also provides a product for diagnosing gliomas. This product can calculate the health probability based on the expression levels of various metabolite markers, and then compare it with reference values ​​to predict or diagnose whether a patient has glioma or is at risk of developing glioma. This product has good feasibility and accuracy, can effectively assess the risk of glioma, and provides a new tool for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 This is the result of the fold difference analysis of the expression levels of significant differential metabolites in the negative ion mode; Figure 2 This is a box plot of the significance of differential metabolites in negative ion mode; Figure 3 is the ROC diagnostic curve. DETAILED DESCRIPTION

[0022] Mass spectrometry (MS) is a highly sensitive analytical technology that identifies the structure and composition of compounds by measuring the mass-to-charge ratio (m / z) of ions. Its core is to convert sample molecules into gas-phase ions and separate and detect them based on their mass differences.

[0023] Positive ion mode (Positive Ion Mode) and negative ion mode (Negative Ion Mode) are two core and optional detection methods in mass spectrometry (MS). The detection mode (positive ion mode or negative ion mode) selected by the detector directly affects the sensitivity and accuracy of the analysis results.

[0024] The similarities between the positive and negative ion modes mentioned above include: ① Sample pretreatment steps (e.g., extraction, centrifugation, and filtration of fecal samples) are generally the same for both modes. ② Liquid chromatography (LC) separation conditions (e.g., columns, mobile phases, gradients, etc.) are generally the same for both modes. ③ Both modes acquire data via mass spectrometry, generating mass spectra and chromatograms for subsequent analysis. ④ The ultimate goal of both modes is to detect and identify metabolites in the sample as comprehensively and accurately as possible.

[0025] The differences between the positive ion mode and negative ion mode mentioned above include: ① Different ionization methods. ② Different types of metabolites detected (positive ion mode is more suitable for detecting basic metabolites (such as amino acids, amines, and some lipids); negative ion mode is more suitable for detecting acidic metabolites (such as organic acids, phenols, and some lipids); ③ Different sensitivity and response: Some metabolites respond more strongly in positive ion mode, while others respond more strongly in negative ion mode. For example, lipids are generally easier to detect in positive ion mode, while organic acids are easier to detect in negative ion mode. ④ Different background noise and interference: Positive ion mode may be more susceptible to interference from matrix effects (such as salts and solvent impurities). Negative ion mode may be more susceptible to interference from carbon dioxide in the air and acidic impurities in the solvent.

[0026] Positive ion metabolites are positively charged, and positive ion mode is used to detect positively charged ions for analysis; negative ion metabolites are negatively charged, and negative ion mode is used to detect negatively charged ions for analysis. For fecal metabolites, the main differences between positive ion mode and negative ion mode are as follows: 1. Because alkaline or neutral-alkaline metabolites are more easily ionized in positive ion mode, positive ion mode is more suitable for detecting alkaline or neutral-alkaline metabolites, such as amino acids (such as leucine and lysine), amines (such as choline and histamine), and some lipids (such as phosphatidylcholine and triglycerides); 2. Because alkaline or neutral-alkaline metabolites are more easily ionized in negative ion mode, negative ion mode is more suitable for detecting alkaline or neutral-alkaline metabolites, such as organic acids (such as citric acid, succinic acid), phenols (such as caffeic acid, ferulic acid), and some lipids (such as fatty acids, phosphatidylserine).

[0027] Gliomas, tumors originating from brain glial cells, are the most common primary intracranial malignancies, accounting for over 7% of all cancer-related deaths. They are characterized by high morbidity and mortality, and a growth pattern that favors local invasion. Therefore, understanding the pathological mechanisms of gliomas is crucial for the effective diagnosis of this deadly tumor. In-depth research on the relationship between gliomas and metabolites may reveal new diagnostic strategies.

[0028] Currently, the methods used to diagnose gliomas are mainly divided into the following categories: computed tomography (CT), magnetic resonance imaging (MRI), and molecular typing diagnosis. The diagnosis of gliomas has entered the molecular era, but the popularization of technology, heterogeneity management, and clinical translation are still bottlenecks that need to be overcome, as explained below: CT scans work by converting the differences in X-ray absorption rates by tissue density in different parts of the body into analog-to-digital images. This process is followed by a computer-generated analog-to-digital conversion, resulting in images with varying grayscale contrast. Glioma density variations, depending on tumor composition, surrounding edema, cystic degeneration, or hemorrhage, can produce images with varying grayscales, ranging from black to white. This allows the diagnostician to determine the nature of the lesion. CT enhancement techniques using intravenous injection of high-density contrast agents are also commonly used in glioma diagnosis. Once the contrast agent enters the blood vessels, it can manifest as varying enhancement patterns depending on the degree of tumor damage to the blood-brain barrier and the differences in blood supply between the tumor and surrounding normal brain tissue. This significantly improves diagnostic performance compared to plain CT scans without contrast agent injection.

[0029] The advent of magnetic resonance imaging (MRI) technology has been revolutionary for the diagnostic imaging of brain tumors, and its value in glioma diagnosis has been widely recognized and applied. Due to differences in imaging principles, MRI offers significantly higher resolution for both normal and pathological tissue than CT, and also offers the advantage of being radiation-free. Low-grade gliomas often lack significant density differences from normal brain tissue, and blood-brain barrier disruption is also minimal. Consequently, unenhanced CT and contrast-enhanced CT are inaccurate for their assessment. MRI scanning not only offers high tissue resolution, but also allows for a general assessment of tissue composition through multiple sequences employing different technical principles. Unlike CT's simple transverse tomographic scans, MRI slices at any angle can provide more anatomical information. Even with unenhanced scans alone (without contrast agent), MRI surpasses CT in overall tumor assessment.

[0030] Molecular typing diagnosis is a method based on molecular biology and bioinformatics technologies that classifies and diagnoses diseases by analyzing the molecular characteristics of samples (such as genomic, transcriptomic, and proteomic data). It shifts the basis for disease classification from traditional pathological characteristics to molecular characteristics, enabling more accurate analysis of disease heterogeneity, thereby providing a basis for personalized diagnosis. Although molecular typing diagnosis is the "gold standard" for diagnosing brain gliomas, it requires obtaining pathological tissue through surgery or biopsy, which carries the risk of trauma, and gene sequencing takes time, which may delay diagnostic decisions. At the same time, gliomas have spatial heterogeneity, and a single biopsy may miss key molecular markers, resulting in inaccurate typing when diagnosing brain gliomas.

[0031] In order to more clearly illustrate the four negative ion salivary metabolite markers in this application, the molecular formulas and structural formulas of the four negative ion salivary metabolite markers involved in the examples are uniformly described below: Heptadecanoic acid: Molecular formula: C 17 H 34 O2 Structural formula:

[0032] Phosphatidylcholine 36:5 (Pc 36:5): Molecular formula: C 44 H 78 NO8P Structural formula:

[0033] 11-dehydrothromboxane b2 Molecular formula: C 20 H 32 O6 Structural formula:

[0034] Acacetin diacetate Molecular formula: C 20 H 16 O7 Structural formula:

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and Examples. The following examples are intended only to illustrate the present invention and are not intended to limit the scope of the invention. The experimental methods in the examples where specific conditions are not specified are generally based on conventional conditions.

[0036] In actual work, in order to evaluate whether the composition of fecal metabolites can be used as a predictive factor for glioma, the present invention collects samples from glioma patients and healthy people, performs metabolome sequencing, and uses bioinformatics to perform statistics on the sequencing data to discover metabolites related to glioma, integrate the metabolites with disease information, and predict glioma patients to the greatest extent.

[0037] The present invention mainly relates to a combination of marker metabolites for predicting or diagnosing brain glioma and its application. The general idea of ​​the whole scheme is that, for brain glioma: Figure 1As shown, we conducted experimental tests on saliva samples (see Example 1 for the specific experimental method) and detected four metabolites with a high correlation with brain glioma. On this basis, we screened out four negative ion metabolites through a preset experimental method (marker screening method and experimental results), and input the relevant quantitative values ​​(expression levels) of the metabolites into the binary logistic regression equation to obtain the logarithm y of the odds of the subject to be tested. Then, the probability Z that the subject to be tested is a healthy person is calculated according to the formula Z = exp(y) / {1 + exp(y)}. The accuracy rate of this detection method is 99.6%.

[0038] Example 1: Sample Collection Saliva samples were collected from 85 patients with brain glioma and 80 healthy subjects. The sample source and inclusion criteria for the glioma group were as follows: Patients were recruited from Zhongnan Hospital. Inclusion criteria included: 1. Age distribution greater than 18 years; 2. Referral for primary brain tumor surgery (resection or biopsy); 3. Patients and their guardians agreed to participate in the study and signed an informed consent form.

[0039] Exclusion criteria for the glioma group: 1. Unable to give consent; 2. Already receiving / currently receiving chemotherapy at the time of MRI; 3. Contraindications to MRI contrast agents; 4. The researchers considered the patient unsuitable for inclusion in this study.

[0040] Control group: Healthy individuals recruited from Wuhan. Inclusion criteria: 1. Age distribution greater than 18 years; 2. Good health, no chronic diseases; 3. No brain glioma; 4. Good eating habits and lifestyle; 5. Good eating habits and lifestyle.

[0041] The exclusion criteria were the same as those for the glioma group.

[0042] Table 1 Sample information table

[0043] Example 2: Sample extraction Saliva was collected according to the standard technique of Navazesh (1993). Participants rinsed their mouths thoroughly with purified water 30 minutes before saliva sampling. Participants were seated comfortably with their eyes open and their head tilted slightly forward for 5 minutes, minimizing oral and facial movements. Saliva was accumulated at the floor of the mouth and spit into a collection tube every 60 seconds. Mix thoroughly and sample directly if clear. If turbid, centrifuge at 3000 g for 10 minutes at 4°C. The supernatant was collected and approximately 100 μl was dispensed into sterile centrifuge tubes for metabolomics. The sample was quickly frozen in liquid nitrogen for 5–10 minutes and stored at −80°C for analysis.

[0044] Example 3: Data Statistics and Analysis 3.1 Experimental Methods S1: After the sample was slowly thawed at 4°C, an appropriate amount of sample was added to a pre-cooled methanol / acetonitrile / water solution (2:2:1, v / v), vortexed, sonicated at low temperature for 30 min, allowed to stand at -20°C for 10 min, centrifuged at 14,000 g for 20 min at 4°C, and the supernatant was vacuum dried. For mass spectrometry analysis, 100 μL of acetonitrile-water solution (acetonitrile: water = 1:1, v / v) was added for reconstitution, vortexed, and centrifuged at 14,000 g for 15 min at 4°C. The supernatant was sampled and analyzed.

[0045] S2: Samples were separated using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system with a HILIC column. The column temperature was 25°C, the flow rate was 0.3 mL / min, and the injection volume was 2 μL. The mobile phase composition was A: water + 25 mM ammonium acetate + 25 mM ammonia, B: acetonitrile. The gradient elution program was as follows: 98% B from 0 to 1.5 min; linear B from 98% to 2% from 1.5 to 12 min; B maintained at 2% from 12 to 14 min; B linear from 2% to 98% from 14 to 14.1 min; B maintained at 98% from 14.1 to 17 min. Samples were kept in an autosampler at 4°C throughout the analysis. To minimize the influence of instrument signal fluctuations, samples were analyzed sequentially in random order. QC (quality control) samples were inserted into the sample queue to monitor and evaluate system stability and the reliability of the experimental data. The QC sample is an equal mixture of all the samples to be tested. The consistency of the QC sample is used to judge the stability of the instrument in sample testing.

[0046] S3: Primary and secondary spectra of the samples were acquired using a Q Exactive series mass spectrometer. After separation on a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system, the samples were analyzed by mass spectrometry on a Q Exactive series mass spectrometer (Thermo). Electrospray ionization (ESI) detection was performed in negative and negative ion modes, respectively. The ESI source and mass spectrometer parameters were as follows: nebulizer gas, auxiliary heater gas 1 (Gas1): 60, auxiliary heater gas 2 (Gas2): 60, curtain gas (CUR): 30 psi, ion source temperature: 600°C, spray voltage (ISVF): ±5500 V (positive and negative modes). The primary mass-to-charge ratio detection range was 80-1200 Da, resolution: 60,000, and scan accumulation time: 100 ms. The secondary acquisition method used a segmented acquisition method with a scan range of 70-1200 Da, secondary resolution: 30,000, scan accumulation time: 50 ms, and dynamic exclusion time: 4 s.

[0047] S4: Raw data were converted to .mzXML format using ProteoWizard, and then peak alignment, retention time correction, and peak area extraction were performed using XCMS software. The data extracted by XCMS were first subjected to metabolite structure identification and data preprocessing, followed by experimental data quality assessment and finally data analysis.

[0048] 3.2. Verification result statistics table The relevant statistical data of the validation set markers are shown in Tables 3 and 4, where the mean determines the center position of the data distribution, while the difference multiple log2 (FC) and the variable weight value VIP reflect the degree of data difference.

[0049] Table 2 - Statistical data of negative ion markers in the validation set

[0050] In Table 2 above, the mean refers to the mean expression level of the corresponding metabolite marker. The log2 fold difference (FC) measures the relative fold change between two data sets. Logarithmic transformation can convert fold relationships into numerical values ​​that are easier to analyze and compare, helping to identify significant differences in the data. The q value is a statistic calculated using the formula for the rank sum test. The variable weight value (VIP) is an important indicator for assessing the importance of metabolite variables and is commonly used when using multivariate statistical methods such as partial least squares discriminant analysis (PLS-DA).

[0051] Table 3 - Relevant expression data of validation set markers

[0052] In practice, metabolite quantification is usually relative, with units of relative intensity or peak area rather than specific physical units. In metabolomics analysis, metabolite expression is often expressed by peak area, and these quantitative values ​​are often relative, used to compare the relative content of metabolites in different samples.

[0053] As can be seen from the data in Tables 2 and 3, the four negative ion differential metabolites of this application are used as detection markers, which are completely non-invasive and highly accurate. This application uses a larger sample size for verification, which makes the prediction of glioma better, and uses metabolomics sequencing to improve the accuracy and reliability of diagnosis. It can provide metabolite detection means for glioma patients and use this to determine the metabolite source basis of the patient's glioma, and also provide a basis for the treatment of glioma in the later stage.

[0054] In summary, the present invention discovered four metabolites, and negative ion metabolites were associated with glioma patients. Among the four metabolites, two showed a significant increasing trend in glioma patients, and two metabolites remained basically unchanged or showed an increasing trend in healthy people.

[0055] Example 4: Data Analysis 4.1 OPLS-DA analysis for biomarker screening Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed significant differences in the metabolic profiles between the glioma and healthy controls. This study used a log2 fold difference (FC) > 1, an OPLS-DA VIP > 1, and a P value < 0.05 as criteria for screening significantly differentially expressed metabolites. Four metabolites were identified in the negative ion mode: heptadecanoic acid, Pc 36:5 (phosphatidylcholine 36:5), 11-dehydrothromboxane B2, and acetin diacetate.

[0056] In actual work, Figure 1 As shown, there are two metabolites upregulated in the disease, namely Heptadecanoic acid and Pc 36:5 (phosphatidylcholine 36:5), and two downregulated metabolites, namely 11-dehydrothromboxane b2 (11-dehydrothromboxane B2) and Acacetin diacetate (acetic willow flavonoid diacetate).

[0057] 4.2 Rank Sum Test The biomarkers mined by OPLS-DA analysis were then subjected to rank sum test, such as Figure 2 As shown in Figure 2, it was found that the four metabolites in negative ion mode were significantly different between the disease and healthy groups. represents P < 0.05; represents P < 0.01; Represents P < 0.001. P is the significance level (p value).

[0058] 4.3 Logistic regression model establishment Based on 80% of the biomarkers mined from the data, the logistic regression algorithm was performed on the four metabolites in the negative ion mode using Rstudio software (referred to as R software) to construct the logistic regression formula of the training model: y=-4.586×10-1 +3.822×10 -8 ×x1+1.392×10 -7 ×x2-1.050×10 -7 ×x3-1.935×10 -7 ×x4; Where y is the odds logarithm of the patient to be tested; Furthermore, the health probability of the patient to be tested is calculated as follows:

[0059] Where Z is the probability that the patient to be tested is a healthy person, exp(y) is the natural exponential function of y, and x1-x9 are the expression levels of the four metabolites; X1:11 - 11-dehydrothromboxane b2 X2: Acacetin diacetate X3: Heptadecanoic acid X4: Phosphatidylcholine 36:5 (Pc 36:5).

[0060] 4.3 ROC Verification Results Based on the data in Table 3 above, a receiver operating characteristic (ROC) curve analysis was performed to obtain the cutoff value (optimal cutoff value).

[0061] Rstudio software was used to calculate specificity and sensitivity and draw the ROC curve. The software first calculated the threshold of the actual measurement value, and then calculated the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold. Specificity (true negative rate) = TN / (TN+FP), sensitivity (true positive rate) = TP / (TP+FN). The ROC curve can be constructed by 1-specificity and sensitivity. The integral of the ROC curve is the AUC (Area Under Curve).

[0062] In order to calculate the specificity and sensitivity of an indicator, the Youden coefficient (Youden index = sensitivity + specificity - 1) is calculated first. The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of the indicator.

[0063] The expression value of a single metabolite marker is directly analyzed by receiver operating characteristic (ROC) curve to obtain the cutoff value (optimal cutoff value). Figure 3 The AUC, optimal cutoff value, sensitivity, and specificity of the mimetic markers and the single metabolite prediction scoring methods are shown in Table 4.

[0064] In the above method, analysis found that negative ion metabolites were associated with glioma patients, and 7 of the above 4 metabolites showed a significant increasing trend in glioma patients, while 2 of the above 4 metabolites remained basically unchanged or showed an increasing trend in healthy people. Specifically, the 2 metabolites that showed a significant increasing trend in glioma patients included: heptadecanoic acid and phosphatidylcholine 36:5 (Pc 36:5); the 2 metabolites that showed a significant decreasing trend in glioma patients included 11-dehydrothromboxane B2 (11-dehydrothromboxaneb2) and acetic acid diacetate.

[0065] Furthermore, validation data from the ROC curve analysis revealed that the negative ion had high specificity and sensitivity as a detection variable, and therefore could be used as a diagnostic marker for the diagnosis of glioma patients. Using these four metabolites as detection markers is completely non-invasive and highly accurate.

[0066] Table 4 ROC diagnostic curve results in negative ion mode

[0067] Example 5 Based on the above embodiments, this embodiment provides a computer program product related to glioma. The computer program product is used to execute a method for diagnosing whether a subject has a risk of glioma, comprising the following steps: 1) obtaining the expression level of a single negative ion salivary metabolite marker in the test subject; wherein the single negative ion salivary metabolite marker comprises any one of heptadecanoic acid, phosphatidylcholine 36:5 (Pc 36:5), 11-dehydrothromboxane B2, and acetin diacetate; 2) Calculate the logarithm y of the odds of the subject to be tested based on the binary logistic regression equation; 3) Calculate the probability Z that the subject is healthy based on y: Z = exp(y) / {1+exp(y)}; where exp(y) is the exponential function of y; 4) Based on the comparison of the probability Z with the reference value, diagnose or predict whether the subject has a brain glioma or has a risk of having a brain glioma.

[0068] In practice, when the Z value is greater than 0.5, it indicates that the probability of the subject suffering from glioma is low; when the Z value is less than 0.5, it indicates that the probability of the subject suffering from glioma is high; when the Z value is 0.5, it means that the subject may be healthy or may have glioma. In this case, further testing is required using other means, such as blood tests and physical signs. Furthermore, the closer the Z value is to 0.5, the more necessary it is to use other means for testing.

[0069] Example 6 Based on the product and method of Example 5, the health probabilities of healthy individuals and glioma patients in the validation set were checked. The specific steps are as follows: 1) Collecting a stool sample from the person to be tested and detecting the expression level of a single negative ion salivary metabolite marker in the stool; wherein the single negative ion salivary metabolite marker includes any one of heptadecanoic acid, phosphatidylcholine 36:5 (Pc 36:5), 11-dehydrothromboxane B2, and acetic acid flavonoid diacetate; 2) Calculate the logarithm y of the odds of the subject to be tested based on the binary logistic regression equation; y=-4.586×10 -1 +3.822×10 -8 ×x1+1.392×10 -7 ×x2-1.050×10 -7 ×x3-1.935×10 -7 ×x4; Wherein, x1 to x4 are the expression levels of 11-dehydrothromboxane b2, acetic acid diacetate, heptadecanoic acid, and phosphatidylcholine 36:5 (Pc 36:5), respectively; 3) Calculate the probability Z of the subject being healthy based on y, Z = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y; 4) Diagnosing or predicting the risk of the subject having brain glioma based on the comparison of the probability Z value of a healthy person with a reference value.

[0070] In practice, when the Z value is greater than 0.5, it indicates that the probability of the subject suffering from glioma is low; when the Z value is less than 0.5, it indicates that the probability of the subject suffering from glioma is high; when the Z value is 0.5, it means that the subject may be healthy or may have glioma. In this case, further testing is required using other means, such as blood tests and physical signs. Furthermore, the closer the Z value is to 0.5, the more necessary it is to use other means for testing.

[0071] Conclusion and explanation: 1. Prediction effect of single metabolites: The negative ion metabolite heptadecanoic acid has the highest prediction effect for glioma, followed by Pc 36:5 (phosphatidylcholine 36:5), 11-dehydrothromboxane b2 (11-dehydrothromboxane B2), and acetin diacetate (acetic willow flavonoid diacetate).

[0072] 2. Prediction effect of mimetic markers: The accuracy of negative ion mimetic markers (markers formed by the combination of multiple biomarkers) is the highest, over 99%, and can provide more accurate prediction of brain glioma syndrome.

[0073] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent replacements and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention should be included in the scope of protection of the invention.

Claims

1. A negative ion salivary metabolite marker associated with brain glioma, characterized in that: The negative ion salivary metabolite markers include 11-dehydrothromboxane B2 and / or acetic acid flavonoid diacetate.

2. The negative ion salivary metabolite marker according to claim 1, wherein The negative ion salivary metabolite markers further include heptadecanoic acid and / or phosphatidylcholine 36:5 (Pc 36:5).

3. The negative ion salivary metabolite marker according to claim 2, wherein The negative ion saliva metabolite markers include heptadecanoic acid, phosphatidylcholine 36:5 (Pc 36:5), 11-dehydrothromboxane B2, and acetic acid flavonoid diacetate.

4. Use of a reagent for detecting negative ion salivary metabolite markers in the preparation of a product for diagnosing or screening brain gliomas. The negative ion salivary metabolite markers include any one or more of heptadecanoic acid, phosphatidylcholine 36:5 (Pc 36:5), 11-dehydrothromboxane B2, and acetic acid flavonoid diacetate.

5. A kit, characterized in that: The invention comprises a detection reagent for detecting the negative ion saliva metabolite marker according to any one of claims 1 to 3.

6. Use of the kit according to claim 5 in preparing a product for detecting brain glioma.

7. Use of the detection reagent in the kit according to claim 5 in preparing a kit for diagnosing brain glioma.

8. A computer program product related to brain glioma, characterized in that: The computer program product is used to diagnose whether a subject has a risk of glioma, comprising the following steps: Obtaining the expression level of a single negative ion salivary metabolite marker in the test subject; wherein the single negative ion salivary metabolite marker includes any one of heptadecanoic acid, phosphatidylcholine 36:5 (Pc 36:5), 11-dehydrothromboxane B2, and acetic acid flavonoid diacetate; Calculate the logarithm y of the odds of the subject to be tested according to the binary logistic regression equation; Calculate the probability Z of the subject being healthy based on y, Z = exp(y) / {1+exp(y)}; where exp(y) is the exponential function of y; Based on the comparison of the probability Z with the reference value, it is diagnosed or predicted whether the subject to be tested suffers from a brain glioma or has a risk of suffering from a brain glioma.

9. The computer program product according to claim 8, wherein: The formula for the binary logistic regression equation is: y=A+B1×x1+B2×x2+B3×x3+B4×x4; Among them, A is the intercept term, B1-B6 are the regression coefficients of the independent variables; x1 to x4 are the expression levels of 11-dehydrothromboxane b2, acetic acid diacetate, heptadecanoic acid, and phosphatidylcholine 36:5 (Pc 36:5), respectively.

10. The computer program product according to claim 9, wherein: The A is -4.586×10 -1 , B1 is 3.822×10 -8 , B2 is 1.392×10 -7 , B3 is -1.050×10 -7 , B4 is -1.935×10 -7 .

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