Negative ion saliva metabolite marker related to meningioma, product and application of negative ion saliva metabolite marker
By screening negative ion saliva metabolite markers and using logistic regression equations, the accuracy and safety issues of meningioma diagnosis were solved, providing a non-invasive and accurate diagnostic tool and overcoming the limitations of existing technologies.
Patent Information
- Application Number
- CN202510431028.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-08
- Publication Date
- 2025-09-26
AI Technical Summary
Existing diagnostic methods for meningiomas suffer from insufficient sensitivity, difficulty in accurate classification, and high risk of invasiveness. Traditional treatment options carry risks of trauma and recurrence, and targeted drugs and immunotherapy are not yet mature. Existing diagnostic and treatment models urgently need breakthroughs in accuracy, safety, and personalization.
Negative ion salivary metabolite markers, including 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine and 3-dehydroepiandrosterone sulfate, are used to screen out acidic or neutral-acidic metabolites related to meningioma through mass spectrometry analysis, and the probability of disease is calculated using a binary logistic regression equation, providing a non-invasive diagnostic tool.
It achieves non-invasive and accurate diagnosis of meningioma, improves the specificity and sensitivity of diagnosis, provides a new diagnostic approach, and reduces the risk of invasive examinations.
Smart Images
Figure CN120703367A_ABST
Abstract
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 meningioma, a product and an application thereof. Background Art
[0002] In current clinical practice, the diagnosis of meningiomas primarily relies on imaging studies (such as CT and MRI) and biopsy. While these techniques can provide some information on tumor localization and histology, they also have significant limitations. First, imaging studies lack sensitivity for early or small lesions, which can lead to missed or delayed diagnosis. Second, the imaging features of different meningioma subtypes overlap significantly, making precise classification difficult and potentially impacting subsequent treatment options. Furthermore, while invasive biopsy remains the gold standard for diagnosis, it carries risks such as surgical trauma, neurological impairment, and sampling error. Surgical resection remains the preferred treatment option, but due to tumor location (such as in high-risk areas like the skull base and parasagittal sinus), complete resection rates are low and the risk of postoperative recurrence is high (approximately 20%-30%). Radiotherapy (such as Gamma Knife) can assist in controlling residual disease but can also cause complications such as radiation-induced cerebral edema and cognitive decline.
[0003] Meanwhile, targeted drugs and immunotherapies for refractory meningiomas are still in the exploratory stage, and traditional chemotherapy regimens suffer from poor specificity and significant toxic side effects. In summary, current diagnostic and treatment models urgently need breakthroughs in terms of accuracy, safety, and personalization.
[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] Positive ion metabolites are positively charged, and the positive ion mode completes the analysis by detecting positively charged ions. The positive ion mode is more suitable for detecting alkaline or neutral to alkaline metabolites, such as: amino acids (such as leucine, lysine), amines (such as choline, histamine), and some lipids (such as phosphatidylcholine, triglycerides); negative ion metabolites are negatively charged, and the negative ion mode completes the analysis by detecting negatively charged ions. The negative ion mode is more suitable for detecting acidic or neutral to acidic 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).
[0006] Therefore, it is necessary to screen out acidic or neutral-acidic metabolites related to glioma based on the negative ion mode, thereby providing a new idea and approach for the diagnosis and treatment of glioma. Summary of the Invention
[0007] In response to the above technical problems, the present invention provides a negative ion saliva metabolite marker related to meningioma, a product and its application to screen out acidic or neutral-acidic metabolites related to meningioma, thereby providing a new idea and approach for the diagnosis of meningioma.
[0008] The technical solutions provided by the present invention are as follows: In a first aspect, a negative ion salivary metabolite marker associated with meningioma is provided, wherein the negative ion salivary metabolite marker includes 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine and / or 3-dehydroepiandrosterone sulfate.
[0009] In the above technical solution, the negative ion salivary metabolite marker also includes one or more of 1,2-dioleoyl-sn-glycero-3-phosphate, 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine, phosphatidylethanolamine 32:1 (Pe 32:1) and phosphatidylethanolamine 34:2 (Pe 34:2).
[0010] In the above technical solution, the negative ion salivary metabolite markers include phosphatidylethanolamine 32:1 (Pe32:1), 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine, phosphatidylethanolamine 34:2 (Pe 34:2), 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine, 3-dehydroepiandrosterone sulfate and 1,2-dioleoyl-sn-glycero-3-phosphate.
[0011] In a second aspect, a reagent for detecting negative ion saliva metabolite markers is provided for use in preparing a product for diagnosing or screening meningioma.
[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 use of the kit described in the third aspect in preparing a product for detecting meningioma.
[0014] In a fifth aspect, there is provided use of the detection reagent in the kit described in the third aspect in preparing a kit for diagnosing meningioma.
[0015] In a sixth aspect, a computer program product related to meningioma is provided, wherein the computer program product is used to diagnose whether a subject has a risk of meningioma, comprising the following steps: Obtaining the expression levels of single negative ion salivary metabolite markers in the test subject; wherein the single negative ion salivary metabolite markers include phosphatidylethanolamine 32:1 (Pe 32:1), 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine, phosphatidylethanolamine 34:2 (Pe 34:2), 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine, 3-dehydroepiandrosterone sulfate, and 1,2-dioleoyl-sn-glycero-3-phosphate; 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 suffers from meningioma or has the risk of suffering from meningioma.
[0016] In one possible implementation, the binary logistic regression equation is formulated as: y=A+B1×x1+B2×x2+B3×x3+B4×x4; Where A is the intercept term, B1 to B6 are the regression coefficients of the independent variables, and x1 to x6 are the expression levels of phosphatidylethanolamine 32:1 (Pe 32:1), 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine, phosphatidylethanolamine 34:2 (Pe 34:2), 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine, 3-dehydroepiandrosterone sulfate, and 1,2-dioleoyl-sn-glycero-3-phosphate, 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 1-stearoyl-2-arachidonoyl-sn-glycerol-3-phosphoserine, phosphatidylethanolamine 34:2, and phosphatidylethanolamine 32:1 of the present invention showed a significant increase in patients with meningioma; the expression levels of the metabolites of 1-stearoyl-2-arachidonoyl-sn-glycerol-3-phosphoserine, 3-dehydroepiandrosterone sulfate, and 1,2-dioleoyl-sn-glycerol-3-phosphate remained basically unchanged or showed an increasing trend in healthy people; ROC curve analysis showed that the six markers described above had high specificity and sensitivity as detection variables. Therefore, these six metabolites can be used as detection markers for the prediction and diagnosis of meningioma patients. Using these six metabolites as detection markers is completely non-invasive and highly accurate.
[0019] 2. The kit of the present invention can use these six metabolites as detection markers to predict or diagnose meningioma, which is completely non-invasive and highly accurate.
[0020] 3. The present invention also provides a product for diagnosing meningioma. 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 meningioma or is at risk of developing meningioma. This product has good feasibility and accuracy, can effectively assess the risk of meningioma, 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
[0021] 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.
[0022] 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.
[0023] 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.
[0024] 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.
[0025] 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).
[0026] In current clinical practice, the diagnosis of meningiomas primarily relies on imaging studies (such as CT and MRI) and biopsy. While these techniques can provide some information on tumor localization and histology, they also have significant limitations. First, imaging studies lack sensitivity for early or small lesions, which can lead to missed or delayed diagnosis. Second, the imaging features of different meningioma subtypes overlap significantly, making precise classification difficult and potentially impacting subsequent treatment options. Furthermore, while invasive biopsy remains the gold standard for diagnosis, it carries risks such as surgical trauma, neurological impairment, and sampling error. Surgical resection remains the preferred treatment option, but due to tumor location (such as in high-risk areas like the skull base and parasagittal sinus), complete resection rates are low and the risk of postoperative recurrence is high (approximately 20%-30%). Radiotherapy (such as Gamma Knife) can assist in controlling residual disease but can also cause complications such as radiation-induced cerebral edema and cognitive decline.
[0027] Meanwhile, targeted drugs and immunotherapies for refractory meningiomas are still in the exploratory stage, and traditional chemotherapy regimens suffer from poor specificity and significant toxic side effects. In summary, current diagnostic and treatment models urgently need breakthroughs in terms of accuracy, safety, and personalization.
[0028] In order to more clearly illustrate the six 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: 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine: Molecular formula: C 44 H 78 NO 10 P Structural formula:
[0029] 1,2-dioleoyl-sn-glycero-3-phosphate Molecular formula: C 39 H 73 O8P Structural formula:
[0030] 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine Molecular formula: C 37 H70 NO8P Structural formula:
[0031] 3-dehydroepiandrosterone sulfate Molecular formula: C 19 H 28 O5S Structural formula:
[0032] Phosphatidylethanolamine 32:1 (Pe 32:1) Molecular formula: C 37 H 72 NO8P Structural formula:
[0033] Phosphatidylethanolamine 34:2 (Pe 34:2) Molecular formula: C 39 H 74 NO8P Structural formula:
[0034] 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.
[0035] In actual work, in order to evaluate whether the composition of fecal metabolites can be used as a predictive factor for meningioma, the present invention collects samples from meningioma patients and healthy people, performs metabolome sequencing, and uses bioinformatics to perform statistics on the sequencing data to discover metabolites related to meningioma, integrate the metabolites with disease information, and predict meningioma patients to the greatest extent.
[0036] The present invention mainly relates to a combination of marker metabolites for predicting or diagnosing meningioma and its application. The general idea of the whole scheme is that, for meningioma: Figure 1 As shown, we conducted experimental tests on saliva samples (see Example 1 for the specific experimental method) and detected six metabolites with a high correlation with meningioma. On this basis, we screened out six 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 85.5%.
[0037] Example 1: Sample Collection Saliva samples were collected from 95 meningiomas and 100 healthy individuals: The meningioma sample source and inclusion criteria 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.
[0038] Exclusion criteria for the meningioma group: 1. Unable to give consent; 2. Patients who had received / were receiving chemotherapy at the time of MRI; 3. Patients who were contraindicated for MRI contrast agents; 4. Patients who were deemed unsuitable for inclusion in this study by the researchers.
[0039] Control group: healthy individuals recruited from Wuhan. Inclusion criteria: 1. Age distribution greater than 18 years; 2. Good health, no chronic diseases; 3. No meningioma-like symptoms; 4. Good eating habits and lifestyle; 5. Good eating habits and lifestyle.
[0040] The exclusion criteria were the same as those for the meningioma group.
[0041] Table 1 Sample information table
[0042] 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.
[0043] 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.
[0044] 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.
[0045] 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.
[0046] 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.
[0047] 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.
[0048] Table 2 - Statistical data of negative ion markers in the validation set
[0049] 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).
[0050] Table 3 - Relevant expression data of validation set markers
[0051] 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.
[0052] As can be seen from the data in Tables 2 and 3, the six negative ion differential metabolites used in this application as detection markers are completely non-invasive and highly accurate. This application uses a larger sample size for verification, which makes the prediction of meningioma better, and uses metabolomics sequencing to improve the accuracy and reliability of diagnosis. It can provide metabolite detection methods for meningioma patients and use them to determine the metabolite source of the patient's meningioma, and also provide a basis for the treatment of meningioma in the later stage.
[0053] In summary, the present invention discovered four metabolites, and negative ion metabolites were associated with meningioma patients. Among the four metabolites, two showed a significant increasing trend in meningioma patients, and two metabolites remained basically unchanged or showed an increasing trend in healthy people.
[0054] 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 meningioma and healthy controls. This study used a log2 fold difference (FC) > 1, OPLS-DA VIP > 1, and a P value < 0.05 as criteria for screening for significantly differentially expressed metabolites. Six metabolites were identified in negative ion mode: heptadecanoic acid, Pc 36:5 (phosphatidylcholine 36:5), 11-dehydrothromboxane B2, and acetin diacetate.
[0055] 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).
[0056] 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, six metabolites in negative ion mode were found to be 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).
[0057] 4.3 Logistic regression model establishment Based on 80% of the biomarkers mined from the data, the logistic regression algorithm was performed on the six 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=-6.997+3.102×10 -8 ×x1-1.496×10 -7 ×x2-3.220×10 -8 ×x3+7.795×10 -6 ×x4+3.173×10 -7 ×x5+3.830×10 -7 ×x6; Where y is the odds logarithm of the patient to be tested; Furthermore, the health probability of the patient to be tested is calculated and is shown as follows: Z = exp(-6.997+3.102×10 -8 ×x1-1.496×10 -7 ×x2-3.220×10 -8 ×x3+7.795×10 -6 ×x4+3.173×10 -7 ×x5+3.830×10 -7 ×x6) / (1+exp(-6.997+3.102×10 -8 ×x1-1.496×10 -7 ×x2-3.220×10 -8 ×x3+7.795×10 -6 ×x4+3.173×10 -7 ×x5+3.830×10 -7 ×x6).
[0058] 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: Phosphatidylethanolamine 32:1 X2:1,2-Dipalmitoleoyl-sn-glycero-3-phosphoethanolamine X3: Phosphatidylethanolamine 34:2 X4: 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine X5:3-Dehydroepiandrosterone sulfate X6: 1,2-dioleoyl-sn-glycerol-3-phosphate.
[0059] 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).
[0060] 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).
[0061] 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.
[0062] The expression value of a single metabolite marker is directly subjected to receiver operating characteristic (ROC) curve analysis to obtain the cutoff value (optimal cutoff value). The ROC curve of the prediction score is as follows: Figure 3 The AUC, optimal cutoff value, sensitivity, and specificity of the mimetic markers and each single metabolite prediction scoring method are shown in Table 4.
[0063] In the above method, analysis found that negative ion metabolites were associated with meningioma patients, and 7 of the above 4 metabolites showed a significant increasing trend in meningioma patients, while 2 of the above 4 metabolites remained basically unchanged or increased in healthy people. Specifically, 3 metabolites showed a significant increasing trend in meningioma patients, including: 1-stearoyl-2-arachidonoyl-sn-glycerol-3-phosphoserine, phosphatidylethanolamine 34:2 and phosphatidylethanolamine 32:1; 3 metabolites remained basically unchanged or increased in healthy people, including: 1-stearoyl-2-arachidonoyl-sn-glycerol-3-phosphoserine, 3-dehydroepiandrosterone sulfate and 1,2-dioleoyl-sn-glycerol-3-phosphate.
[0064] 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 meningioma patients. Using these six metabolites as detection markers is completely non-invasive and highly accurate.
[0065] Table 4 ROC diagnostic curve results in negative ion mode
[0066] Example 5 Based on the above embodiments, this embodiment provides a computer program product related to meningioma, wherein the computer program product is used to execute a method for diagnosing whether a subject has a risk of meningioma, including 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 includes 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) Diagnosing or predicting whether the subject has meningioma or is at risk of having meningioma based on the comparison of the probability Z with the reference value.
[0067] In practice, when the Z value is greater than 0.5, it indicates that the probability of the subject having meningioma is low; when the Z value is less than 0.5, it indicates that the probability of the subject having meningioma is high; and when the Z value is 0.5, it means that the subject may be healthy or may have meningioma. In this case, further testing is necessary 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.
[0068] Example 6 Based on the product and method of Example 5, the health probabilities of healthy individuals and meningioma patients in the validation set were checked. The specific steps are as follows: 1) Collecting stool samples from the subjects to be tested and detecting the expression levels of single negative ion salivary metabolite markers in the stool; wherein the single negative ion salivary metabolite markers include 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 meningioma based on the comparison of the probability Z value of a healthy person with a reference value.
[0069] In practice, when the Z value is greater than 0.5, it indicates that the subject has a low probability of meningioma; when the Z value is less than 0.5, it indicates that the subject has a high probability of meningioma; and when the Z value is 0.5, it means that the subject may be healthy or may have meningioma. In this case, further testing is necessary 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.
[0070] Conclusion and explanation: 1. Single metabolite prediction effect: The negative ion metabolite 3-dehydroepiandrosterone sulfate has the highest effect in predicting meningioma, followed by 1,2-dioleoyl-sn-glycerol-3-phosphate.
[0071] 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 80%, and can provide more accurate prediction of meningioma syndrome.
[0072] 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 meningioma, characterized in that: The negative ion salivary metabolite markers include 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine and / or 3-dehydroepiandrosterone sulfate.
2. The negative ion salivary metabolite marker according to claim 1, wherein The negative ion salivary metabolite markers also include one or more of 1,2-dioleoyl-sn-glycero-3-phosphate, 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine, phosphatidylethanolamine 32:1 (Pe 32:1) and phosphatidylethanolamine 34:2 (Pe 34:2).
3. The negative ion salivary metabolite marker according to claim 2, wherein The negative ion salivary metabolite markers include phosphatidylethanolamine 32:1 (Pe 32:1), 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine, phosphatidylethanolamine 34:2 (Pe34:2), 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine, 3-dehydroepiandrosterone sulfate, and 1,2-dioleoyl-sn-glycero-3-phosphate.
4. Use of a reagent for detecting the negative ion salivary metabolite marker according to any one of claims 1 to 3 in the preparation of a product for diagnosing or screening meningioma.
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 meningioma.
7. Use of the detection reagent in the kit according to claim 5 in preparing a kit for diagnosing meningioma.
8. A computer program product related to meningioma, characterized in that: The computer program product is used to diagnose whether a subject has a risk of meningioma, comprising the following steps: Obtaining the expression levels of single negative ion salivary metabolite markers in the test subject; wherein the single negative ion salivary metabolite markers include phosphatidylethanolamine 32:1 (Pe 32:1), 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine, phosphatidylethanolamine 34:2 (Pe 34:2), 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine, 3-dehydroepiandrosterone sulfate, and 1,2-dioleoyl-sn-glycero-3-phosphate; 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 suffers from meningioma or has the risk of suffering from meningioma.
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+B5×x5+B6×x6; Where A is the intercept term, B1 to B6 are the regression coefficients of the independent variables, and x1 to x6 are the expression levels of phosphatidylethanolamine 32:1 (Pe32:1), 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine, phosphatidylethanolamine 34:2 (Pe34:2), 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine, 3-dehydroepiandrosterone sulfate, and 1,2-dioleoyl-sn-glycero-3-phosphate, respectively.
10. The computer program product according to claim 9, wherein: A is -6.997, B1 is 3.102×10 -8 , B2 is -1.496×10 -7 , B3 is -3.220×10 -8 , B4 is 7.795×10 -6 , B5 is 3.173×10 -7 , B6 is 3.830×10 -7 .