Positive ion saliva metabolite marker related to meningioma, product and application of positive ion saliva metabolite marker

Abstract

The invention discloses a positive ion saliva metabolite marker related to meningioma, a product and application of the positive ion saliva metabolite marker, and relates to the field of biological medicine. The positive ion saliva metabolite marker is prepared from one or more of 1-palmitoyl-2-glutaryl phosphatidylcholine, 2, 3, 5-triiodobenzene alcohol, beta-estradiol, cortisol, N-acetylcadaverine, prilocaine, octadecanoic acid, sulfamethoxam and rimsulfuron. The invention provides a kit which is used for detecting positive ion saliva metabolite markers related to meningioma and comprises a specific detection reagent. In addition, the invention also provides a computer program product which is specially used for evaluating meningioma. The products have good feasibility and accuracy, the risk of meningioma 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 meningioma positive ion saliva metabolite marker, 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 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).

[0006] Therefore, it is necessary to screen out alkaline or neutral alkaline metabolites related to glioma based on the positive ion mode, so as to provide 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 marker, product and application of positive ion salivary metabolites related to meningioma to screen out alkaline or neutral-alkaline 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 positive ion salivary metabolite marker associated with meningioma is provided, characterized in that the positive ion salivary metabolite marker includes one or more of 1-palmitoyl-2-glutarylphosphatidylcholine, 2,3,5-triiodobenzyl alcohol and N-acetylcadaverine.

[0009] In the above technical solution, the positive ion salivary metabolite markers further include one or more of β-estradiol, cortisol, prilocaine, stearidonic acid, sulfafenamide and tilamide.

[0010] In the above technical solution, the positive ion salivary metabolite markers include 1-palmitoyl-2-glutaryl phosphatidylcholine, 2,3,5-triiodobenzyl alcohol, β-estradiol, cortisol, N-acetylcadaverine, prilocaine, stearidonic acid, sulfafenamide and tilamide.

[0011] In a second aspect, a reagent for detecting positive ion salivary 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 positive 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 a use of a detection reagent in the kit of the third aspect in the preparation of a kit for diagnosing meningioma.

[0015] In a sixth aspect, there is provided a computer program product related to meningioma, which is used to execute a diagnosis of whether a subject has a risk of suffering from meningioma, comprising the following steps: obtaining the expression level of a single positive ion saliva metabolite marker of the subject; wherein the single positive ion saliva metabolite marker comprises 1-palmitoyl-2-glutaryl phosphatidylcholine, 2,3,5-triiodobenzyl alcohol, Beta-estradiol, Cortisol, N-acetylcadaverine, Prilocaine, Stearidonic acid, Sulfallate and Tillam; calculating the log odds y of the subject according to a binary logistic regression equation; calculating the probability Z of the subject being a healthy person according to y, Z = exp (y) / {1 + exp (y)}; wherein exp (y) is the exponential function of y; diagnosing or predicting whether the subject has meningioma or has a risk of suffering from meningioma according to the comparison between the probability Z and a reference value.

[0016] In one possible implementation, the formula of the binary logistic regression equation is: y = A + B1 x x1 + B2 x x2 + B3 x x3 + B4 x x4 + B5 x x5 + B6 x x6 + B7 x x7 + B8 x x68 + B9 x x9; wherein A is the intercept term, B1-B6 are the regression coefficients of the independent variables; x1-x9 are the expression levels of 1-palmitoyl-2-glutaryl phosphatidylcholine, 2,3,5-triiodobenzyl alcohol, Beta-estradiol, Cortisol, N-acetylcadaverine, Prilocaine, Stearidonic acid, Sulfallate and Tillam, respectively.

[0017] Furthermore, the A is 6.970×10 -2 , B1 is -2.754×10 -7 , B2 is -9.419×10 -9 , B3 is -1.649×10 -7 , B4 is 5.533×10 -7 , B5 is 9.372×10 -9 , B6 is 1.655×10 -8 , B7 is -1.968×10 -7 , B8 is 3.517×10 -8 , B9 is -1.056×10 -7 .

[0018] The beneficial effects of the present invention are as follows: 1. ROC curve analysis shows that the nine markers described above have high specificity and sensitivity as detection variables. Therefore, these nine metabolites can be used as detection markers for the prediction and diagnosis of meningioma patients. Using these nine metabolites as detection markers is completely non-invasive and highly accurate.

[0019] 2. The kit of the present invention can predict or diagnose meningioma using these nine metabolites as detection markers, 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 significantly different metabolites in the positive ion mode; Figure 2 This is a box plot of significant differential metabolites in positive 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 ion mode and the negative ion mode mentioned above include: (1) Whether in positive ion mode or negative ion mode, the sample pretreatment steps (such as extraction, centrifugation, filtration, etc. of fecal samples) are generally the same. (2) The liquid chromatography (LC) separation conditions (such as chromatographic columns, mobile phases, gradients, etc.) used in both modes are generally the same. (3) Both modes acquire data through mass spectrometry to generate mass spectra (MS spectra) and chromatograms (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 the negative ion mode mentioned above include: (1) different ionization modes. (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 meningioma mainly relies on imaging examinations (such as CT, MRI) and pathological biopsy. Although these technologies can provide certain tumor localization and histological information, they still have significant limitations: First, imaging examinations are not sensitive enough to detect early or small lesions, which can easily lead to missed or delayed diagnosis; Secondly, the imaging features of different subtypes of meningiomas have a high degree of overlap, making it difficult to accurately classify them, which may affect the choice of subsequent treatment options.

[0027] In addition, although invasive biopsy is the gold standard for diagnosis, it carries risks such as surgical trauma, neurological damage, and sampling errors.

[0028] At the treatment level, surgical resection remains the preferred option, but due to the location of the tumor (such as high-risk areas such as the skull base and parasagittal sinus), the complete resection rate is low and the risk of postoperative recurrence is high (about 20%-30%).

[0029] Although radiotherapy (such as gamma knife) can help control residual lesions, it may cause complications such as radiation-induced cerebral edema and cognitive decline.

[0030] At the same time, targeted drugs and immunotherapies for refractory meningiomas are still in the exploratory stage, and traditional chemotherapy regimens have problems such as poor specificity and significant toxic side effects.

[0031] In summary, the existing diagnosis and treatment model urgently needs breakthroughs in accuracy, safety and personalization.

[0032] In order to more clearly illustrate the 9 positive ion salivary metabolite markers in this application, the molecular formulas and structural formulas of the 9 positive ion salivary metabolite markers involved in the examples are uniformly described below: 1-palmitoyl-2-glutarylphosphatidylcholine Molecular formula: C 29 H 56 NO 10 P Structural formula:

[0033] 2,3,5-triiodobenzyl alcohol (2,3,5-triiodobenzyl alcohol) molecular formula: C7H5I3O Structural formula:

[0034] Beta-estradiol molecular formula: C 18 H24 O2 structural formula:

[0035] Cortisol Molecular formula: C 21 H 30 O5 Structural formula:

[0036] N-acetylcadaverine Molecular formula: C7H 16 N2O Structural formula:

[0037] Prilocaine Molecular formula: C 13 H 20 N2O Structural formula:

[0038] Stearidonic acid Molecular formula: C 18 H 28 O2 Structural formula:

[0039] Sulfallate Molecular formula: C15H10ClF3N2O6S Structural formula:

[0040] Sulfosuccinate-methyl (Tillam) Molecular formula: C 10 H 21 NOS Structural formula:

[0041] 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.

[0042] 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.

[0043] 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 (for specific experimental methods, refer to Examples 1-2) and detected 9 positive ion metabolites with a high correlation with meningioma. On this basis, we screened out 9 positive 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 99.7%.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] The exclusion criteria were the same as those for the meningioma group.

[0048] Table 1 Sample information table

[0049] 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.

[0050] Example 3: Data statistics and analysis 3.1, Experimental method 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), vortex mixed, ultrasonically treated at low temperature for 30 min, stood at -20°C for 10 min, centrifuged at 14000 g at 4°C for 20 min, the supernatant was vacuum dried, 100 μL of acetonitrile water solution (acetonitrile: water = 1:1, v / v) was added for reconstitution before mass spectrometry analysis, vortexed, centrifuged at 14000 g at 4°C for 15 min, and the supernatant was injected for analysis.

[0051] S2: The sample was separated by Vanquish LC ultra-high performance liquid chromatography system (UHPLC) HILIC column; column temperature 25°C; flow rate 0.3 mL / min; injection volume 2 μL; mobile phase composition A: water + 25 mM ammonium acetate + 25 mM ammonia water, B: acetonitrile; gradient elution program as follows: 0---1.5 min, 98% B; 1.5---12 min, B from 98% linearly changed to 2%; 12---14 min, B maintained at 2%; 14---14.1 min, B from 2% linearly changed to 98%; 14.1--17 min, B maintained at 98%; during the whole analysis process, the sample was placed in the 4°C automatic sampler. In order to avoid the influence of instrument detection signal fluctuation, random order was used for continuous analysis of samples. QC (Quality Control) samples were inserted in the sample queue for monitoring and evaluating the stability of the system and the reliability of the experimental data. QC samples were mixed with all the detected samples in equal amounts, and the consistency of QC samples was used to judge the stability of the instrument in sample detection.

[0052] 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 both positive and negative ion modes. 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, resolution: 30,000, scan accumulation time: 50 ms, and dynamic exclusion time: 4 s.

[0053] 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.

[0054] 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.

[0055] Table 2 - Statistical data of positive ion markers in the validation set

[0056] 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 p-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).

[0057] Table 3 - Relevant expression data of validation set markers

[0058] 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 usually relative, used to compare the relative content of metabolites in different samples.

[0059] As can be seen from the data in Tables 2 and 3, the 9 positive 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 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.

[0060] In summary, the present invention discovered 9 metabolites, and positive ion metabolites were associated with meningioma patients. Among the above 9 metabolites, 6 metabolites showed a significant increasing trend in meningioma patients, and 3 metabolites remained basically unchanged or showed an increasing trend in healthy people.

[0061] Example 4: Data Analysis 4.1 OPLS-DA analysis for biomarker screening Orthogonal partial least squares discriminant analysis (OPLS-DA) showed significant differences in the metabolic profiles between the meningioma group and the healthy control group. This study used a log2 fold difference (FC) > 1, OPLS-DA VIP > 1, and a P value < 0.05 as screening criteria for significantly differentially expressed metabolites. Nine metabolites were identified in positive ion mode between meningioma and healthy controls: 1-palmitoyl-2-glutaryl phosphatidylcholine, 2,3,5-triiodobenzyl alcohol, β-estradiol, cortisol, N-acetylcadaverine, prilocaine, stearidonic acid, sulfallate, and tilamide.

[0062] In actual work, Figure 1As shown, 6 metabolites were up-regulated in the disease, and 3 metabolites were down-regulated, of which: 6 metabolites showed a significant increasing trend in meningioma patients, including: 1-palmitoyl-2-glutaryl phosphatidylcholine, 2, 3, 5-triiodobenzyl alcohol, N-acetylcysteine, propylamine, stearidonic acid and sulfonamide; 3 metabolites remained basically unchanged or showed an increasing trend in healthy people, including: β-estradiol, cortisol and sulfonamide.

[0063] 4.2 Rank sum test According to the OPLS-DA analysis of the mined biomarkers, the rank sum test was performed, as shown in Table 2. Figure 2 As shown, 9 metabolites in positive ion mode were found to be significantly different between the disease and healthy groups. Note: P < 0.05; P < 0.01; P < 0.001. P is the significance level (p value).

[0064] 4.3 Establishment of logistic regression model Through 80% of the data mining biomarkers, the logistic regression algorithm was used to construct the training model of 9 metabolites in positive ion mode using Rstudio software (referred to as R software): y = 6.970 × 10 -2 -2.754 × 10 -7 × x1-9.419 × 10 -9 × x2-1.649 × 10 -7 × x3+5.533 × 10 -7 × x4+9.372 × 10 -9 × x5+1.655 × 10 -8 × x6-1.968 × 10 -7 × x7+3.517 × 10 -8 × x8-1.056 × 10 -7 × x9; Where y is the odds ratio of the patient to be tested; Further, the health probability of the patient to be tested was calculated, as shown below: Z = exp (6.970 × 10 -2 -2.754 × 10 -7 × x1-9.419 × 10 -9 × x2-1.649 × 10 -7 × x3+5.533 × 10 -7 × x4+9.372 × 10 -9×x5+1.655×10 -8 ×x6-1.968×10 -7 ×x7+3.517×10 -8 ×x8-1.056×10 -7 ×x9) / (1+exp(6.970×10 -2 -2.754×10 -7 ×x1-9.419×10 -9 ×x2-1.649×10 -7 ×x3+5.533×10 -7 ×x4+9.372×10 -9 ×x5+1.655×10 -8 ×x6-1.968×10 -7 ×x7+3.517×10 -8 ×x8-1.056×10 -7 ×x9).

[0065] 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 nine metabolites; X1: 1-palmitoyl-2-glutarylphosphatidylcholine; X2: 2,3,5-triiodobenzyl alcohol; X3: β-estradiol; X4: cortisol; X5: N-acetylcadaverine; X6: Prilocaine; X7: octadecatetraenoic acid; X8: sulfafenamide; X9: Sulfonamide.

[0066] 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).

[0067] 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).

[0068] 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.

[0069] 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.

[0070] In the above method, analysis found that positive ion metabolites were associated with meningioma patients, and 7 of the above 9 metabolites showed a significant increasing trend in meningioma patients, while 2 of the above 9 metabolites remained basically unchanged or increased in healthy people. Specifically, 9 metabolites showed a significant increasing trend in meningioma patients, including: 1-palmitoyl-2-glutarylphosphatidylcholine, 2,3,5-triiodobenzyl alcohol, N-acetylcadaverine, prilocaine, stearidonic acid and sulfamethoxazole; 3 metabolites remained basically unchanged or increased in healthy people, including: β-estradiol, cortisol and sulfamethoxazole.

[0071] Furthermore, validation data from the ROC curve analysis revealed high specificity and sensitivity as a detection variable, suggesting its applicability as a diagnostic marker for meningioma patients. Using these nine positive ion metabolites as diagnostic markers is completely noninvasive and highly accurate.

[0072] Table 4 ROC diagnostic curve results in positive ion mode

[0073] 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 positive ion salivary metabolite marker in the test subject; wherein the single positive ion salivary metabolite marker includes 1-palmitoyl-2-glutarylphosphatidylcholine, 2,3,5-triiodobenzyl alcohol, beta-estradiol, cortisol, N-acetylcadaverine, prilocaine, stearidonic acid, sulfallate, and tilamide; 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.

[0074] 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.

[0075] 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) Collect stool samples from the subjects to be tested and detect the expression levels of single positive ion salivary metabolite markers in the stool; wherein the single positive ion salivary metabolite markers include 1-palmitoyl-2-glutaryl phosphatidylcholine, 2,3,5-triiodobenzyl alcohol, Beta-estradiol, Cortisol, N-acetylcadaverine, Prilocaine, Stearidonic acid, Sulfallate, and Tillam; 2) Calculate the logarithm y of the odds of the subject to be tested based on the binary logistic regression equation; y=6.970×10 -2 -2.754×10 -7 ×x1-9.419×10 -9 ×x2-1.649×10 -7 ×x3+5.533×10 -7 ×x4+9.372×10 -9 ×x5+1.655×10 -8 ×x6-1.968×10 -7 ×x7+3.517×10 -8 ×x8-1.056×10 -7 ×x9; Wherein, x1 to x9 are the expression levels of 1-palmitoyl-2-glutarylphosphatidylcholine, 2,3,5-triiodobenzyl alcohol, β-estradiol, cortisol, N-acetylcadaverine, prilocaine, stearidonic acid, sulfafenamide, and tilam, 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.

[0076] In actual work, when the Z value is greater than 0.5, it indicates that the probability of the to-be-tested object suffering from meningioma is small; when the Z value is less than 0.5, it indicates that the probability of the to-be-tested object suffering from meningioma is large; when the Z value is 0.5, it indicates that the to-be-tested object may be a healthy person or a meningioma patient, at this time, further detection needs to be performed by using other means, and the other means are blood routine and judgment of physical signs. Further, the closer the Z value is to 0.5, the more other means need to be used for detection.

[0077] Conclusion and explanation: 1. Single metabolite prediction effect: among the positive ion metabolites, 1-palmitoyl-2-glutaryl phosphatidylcholine and cortisol (Cortisol) have the best effect in predicting meningioma, followed by Beta-estradiol and N-acetylcadaverine.

[0078] 2. Mimic marker prediction effect: the accuracy of the positive ion mimic marker (a marker formed by the combination of multiple biomarkers) is the highest, more than 99%, which can provide more accurate prediction of meningioma syndrome.

[0079] The above merely describes preferred specific embodiments of the present application, but the scope of protection of the present application is not limited thereto, and any modification, equivalent replacement and improvement made by any person skilled in the art within the technical range disclosed by the present application shall be included in the scope of protection of the present application.

Claims

1. A positive ion salivary metabolite marker associated with meningioma, characterized in that: The positive ion salivary metabolite markers include one or more of 1-palmitoyl-2-glutarylphosphatidylcholine, 2,3,5-triiodobenzyl alcohol, and N-acetylcadaverine.

2. The positive ion salivary metabolite marker according to claim 1, wherein The positive ion salivary metabolite markers further include one or more of β-estradiol, cortisol, prilocaine, stearidonic acid, sulfafenamide, and tilamide.

3. The positive ion salivary metabolite marker according to claim 2, characterized in that The positive ion salivary metabolite markers include 1-palmitoyl-2-glutarylphosphatidylcholine, 2,3,5-triiodobenzyl alcohol, Beta-estradiol, Cortisol, N-acetylcadaverine, Prilocaine, Stearidonic acid, Sulfallate and Tillam.

4. Use of a reagent for detecting the positive 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 positive ion salivary 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 level of a single positive ion salivary metabolite marker in the test subject; wherein the single positive ion salivary metabolite marker includes 1-palmitoyl-2-glutarylphosphatidylcholine, 2,3,5-triiodobenzyl alcohol, β-estradiol, cortisol, N-acetylcadaverine, prilocaine, stearidonic acid, sulfafenamide, and tilamide; 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+B7×x7+B8×x68+B9×x9; Among them, A is the intercept term, B1 to B6 are the regression coefficients of the independent variables; x1 to x9 are the expression levels of 1-palmitoyl-2-glutaryl phosphatidylcholine, 2,3,5-triiodobenzyl alcohol, β-estradiol, cortisol, N-acetylcadaverine, prilocaine, stearidonic acid, sulfallate, and tilam, respectively.

10. The computer program product according to claim 9, wherein: The A is 6.970×10 -2 , B1 is -2.754××10 -7 , B2 is -9.419×10 -9 , B3 is -1.649×10 -7 , B4 is 5.533×10 -7 , B5 is 9.372×10 -9 , B6 is 1.655×10 -8 , B7 is -1.968×10 -7 , B8 is 3.517×10 -8 , B9 is -1.056×10 -7 .

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