Negative ion saliva marker related to brain glioma and meningioma and application of negative ion saliva marker

Abstract

The invention discloses a negative ion saliva marker related to brain glioma and meningioma and application of the negative ion saliva marker, and relates to the field of biological medicine. The negative ion saliva marker is prepared from one or more of 1, 2-dioleoyl-sn-glycerol-3-phosphoric acid, N-acetyl-L-glutamic acid, arabitol, myristic acid, linolenic acid and hexanoic acid. The invention provides a kit, which is used for detecting negative ion saliva markers related to brain glioma and meningioma and comprises a specific detection reagent. Besides, the invention further provides a computer program product which can judge whether the patient to be detected is a glioma patient or a meningioma patient, so that a solid foundation is laid for subsequent treatment. The method has good feasibility and accuracy, is safe and noninvasive, can effectively distinguish brain glioma and meningioma, and provides a brand new powerful tool for clinical diagnosis.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biological medicine, and particularly relates to a negative ion saliva marker related to brain glioma and meningioma and application thereof. BACKGROUND

[0002] Brain glioma and meningioma both belong to subtypes of intracranial tumors, but there is a significant difference in the harmfulness of brain glioma and meningioma to the human body. Brain glioma is a tumor originating from brain neuroglia cells, which is mostly malignant and highly invasive. Meningioma is a tumor originating from the meninges (membrane covering the surface of the brain and spinal cord), which is mostly benign and has clear boundaries.

[0003] In current clinical practice, imaging detection is the core detection means for brain glioma and meningioma (such as computed tomography CT and magnetic resonance imaging MRI), which has the following significant limitations in clinical application: 1) Imaging examination has insufficient sensitivity to early or small lesions, which easily leads to missed diagnosis or delayed diagnosis; 2) Low-grade brain glioma (such as grade II astrocytoma) may appear as a lesion with a relatively clear boundary and no enhancement, especially in the case of meningioma combined with cystic degeneration or calcification; 3) Atypical meningioma (WHO grade II) can appear infiltrative growth, uneven enhancement and peripheral edema, which is similar to high-grade glioma (such as anaplastic astrocytoma) in imaging performance.

[0004] In the case that imaging detection cannot distinguish brain glioma and meningioma, people often distinguish brain glioma and meningioma through invasive biopsy (i.e. pathological biopsy), but invasive biopsy, although being the gold standard for diagnosis, has the risks of surgical trauma, nerve function damage and sampling error.

[0005] Therefore, in the existing methods for distinguishing brain glioma and meningioma, imaging detection has the problem that the accuracy of the detection result needs to be further improved, and invasive biopsy (i.e. pathological biopsy) has the problem that there is a risk of surgical trauma (even the risk of brain nerve function damage) when detecting, so it is necessary to improve it. SUMMARY

[0006] In view of the above technical problems, the present application provides a negative ion saliva marker related to brain glioma and meningioma and application thereof based on the negative ion mode in mass spectrometry (MS), so as to provide a new idea and approach for distinguishing brain glioma and meningioma.

[0007] The technical solution provided by the present application is as follows: In a first aspect, a positive ion saliva marker related to brain glioma and meningioma is provided, and the negative ion saliva marker comprises 1,2-dioleoyl-sn-glycero-3-phosphate and / or N-acetyl-l-glutamate.

[0008] In the above technical solution, the negative ion saliva marker further comprises one or more of D-arabitol, Myristic acid, alpha-Linolenic acid, and Caproic acid.

[0009] In the above technical solution, the negative ion saliva marker comprises 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-l-glutamate, D-arabitol, Myristic acid, alpha-Linolenic acid, and Caproic acid.

[0010] In a second aspect, an application of a reagent for detecting the negative ion saliva marker of the first aspect in preparing a product for diagnosing brain glioma is provided.

[0011] In a third aspect, a kit is provided, and the kit contains a reagent for detecting the negative ion saliva marker of the first aspect.

[0012] In a fourth aspect, an application of the kit of the third aspect in preparing a product related to brain glioma and meningioma is provided.

[0013] In a fifth aspect, a product for diagnosing brain glioma is provided, and the product comprises primers, probes, antibodies, aptamers, or chips specific to the negative ion saliva marker of the first aspect.

[0014] In a sixth aspect, a computer program product is provided, and the computer program product is used to execute a method of determining whether a patient to be tested is a brain glioma patient or a meningioma patient, and the method comprises the following steps: Obtaining the expression amount of a single negative ion saliva marker of the patient to be tested; the expression amount of each single negative ion saliva marker, including 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-l-glutamate, D-arabitol, Myristic acid, alpha-Linolenic acid and Caproic acid, into a binary logistic regression equation to calculate the logarithm y of the advantage of the patient to be tested; According to y, calculate the probability Z that the patient to be tested is a brain glioma patient, Z = exp(y) / {1 + exp(y)}; wherein, exp(y) is the exponential function of y; According to the comparison of the probability Z and the reference value, determine which one of the brain glioma patient and the meningioma patient the patient to be tested is.

[0015] In the above technical solution, the formula of the binary logistic regression equation is: y = A + B1x1 + B2x2 + B3x3 + B4x4 + B5x5 + B6x6; wherein, A is the intercept term, B1-B6 are the regression coefficients of the independent variables; x1 is the expression amount of 1,2-dioleoyl-sn-glycero-3-phosphate; x2 is the expression amount of D-arabitol; x3 is the expression amount of Myristic acid; x4 is the expression amount of alpha-Linolenic acid; x5 is the expression amount of N-acetyl-l-glutamate; x6 is the expression amount of Caproic acid.

[0016] In the above technical solution, A is , B1 is , B2 is , B3 is , B4 is , B5 is , and B6 is .

[0017] It should be noted that the patient to be tested in the present application is a patient who is preliminarily diagnosed as a brain glioma or a meningioma by medical personnel through imaging detection. The present application aims to be applied to help medical personnel further distinguish and diagnose which one of a brain glioma or a meningioma the patient to be tested is, especially in the case where the patient is unwilling to undergo invasive biopsy (invasive biopsy has the risk of surgical trauma and nerve function damage).

[0018] The beneficial effects of the present application are as follows: 1. The present application provides a negative ion saliva marker related to brain glioma and meningioma and its application, so as to screen acid compounds (or slightly acidic compounds) related to brain glioma and meningioma from the saliva of the patient to be tested, so as to help medical staff to distinguish which one of brain glioma and meningioma the patient to be tested is, which has good feasibility and accuracy, safety and non-invasiveness. Medical staff can detect and diagnose the saliva of the patient by using the single or multiple negative ion saliva markers discovered in the present application, so as to determine which one of brain glioma and meningioma the patient to be tested is. The present application can be used as a new idea and way to distinguish brain glioma and meningioma, and provides a new powerful tool for clinical diagnosis.

[0019] 2. The present application is based on the negative ion mode in mass spectrometry (MS), and six acid (or slightly acid) compounds related to brain glioma and meningioma which can be detected in the saliva of the patient are discovered, which include 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-l-glutamate, D-arabitol, myristic acid, alpha-Linolenic acid and caproic acid. Through experimental analysis and verification (see embodiments 3 and 4), the above six negative ion compounds have high specificity and sensitivity as detection variables, and can be used as detection markers for diagnosing which one of brain glioma and meningioma the patient to be tested is.

[0020] 3. Further research shows that among the above six negative ion compounds, four compounds (metabolites) have higher content in brain glioma patients, including 1,2-dioleoyl-sn-glycero-3-phosphate, D-arabitol, myristic acid and alpha-Linolenic acid; two metabolites have higher content in meningioma patients, including N-acetyl-l-glutamate and caproic acid.

[0021] 4. The present application also provides a reagent and a kit, which can use the above six negative ion compounds as detection markers, and can be used to distinguish which one of brain glioma and meningioma the patient to be tested is, which is completely non-invasive and has high accuracy. At the same time, the six negative ion saliva markers can also be used as target microorganisms for the development of these systems, filling the gap in this field.

[0022] 5. The application also provides a product for distinguishing brain glioma and meningioma, which can calculate the probability of the patient being a brain glioma patient based on the expression amount of each acidic compound, and then compare with the reference value, so as to help medical staff judge which one the patient is, brain glioma patient or meningioma patient. The product has good feasibility and accuracy, and can effectively distinguish brain glioma patients and meningioma patients, providing a new tool and idea for clinical diagnosis. BRIEF DESCRIPTION OF DRAWINGS Figure 1 Figure 3 is a negative ion mode significant difference marker expression difference fold analysis result graph; Figure 2 Figure 4 is a negative ion mode difference marker significance box plot; Figure 3 Figure 5 is a ROC diagnostic curve. DETAILED DESCRIPTION

[0023] Mass spectrometry (MS) is a high-sensitivity analysis technique for identifying the structure and composition of compounds by measuring the mass-to-charge ratio (m / z) of ions. The core of the technique is to convert sample molecules into gas-phase ions and separate and detect them according to their mass differences.

[0024] Positive ion mode and negative ion mode are two core and alternative detection methods in mass spectrometry (MS). The detection mode (positive ion mode or negative ion mode) selected by the detection personnel directly affects the sensitivity and accuracy of the analysis results.

[0025] The same points of the above-mentioned positive ion mode and negative ion mode include: (1) Whether it is positive ion mode or negative ion mode, the pretreatment steps of the sample (such as extraction, centrifugation, filtration of saliva sample, etc.) are usually consistent. (2) The liquid chromatography (LC) separation conditions (such as chromatographic column, mobile phase, gradient, etc.) used in the two modes are usually the same. (3) Both modes collect data through mass spectrometry to generate mass spectra (MS spectra) and chromatograms (chromatograms) for subsequent analysis. (4) The ultimate goal of the two modes is to detect and identify as many compounds in the sample as possible and as accurately as possible.

[0026] The differences between the above-mentioned positive ion mode and negative ion mode include: (1) different ionization methods; (2) different types of compounds detected (the positive ion mode is more suitable for detecting basic compounds (such as amino acids, amines, and some lipids; the negative ion mode is more suitable for detecting acidic compounds (such as organic acids, phenols, and some lipids); (3) different sensitivities and responses: some compounds respond more strongly in the positive ion mode, while others respond more strongly in the negative ion mode. For example, lipids are generally easier to detect in the positive ion mode, while organic acids are easier to detect in the negative ion mode; (4) different background noise and interference: the positive ion mode may be more susceptible to interference from matrix effects (such as salts and solvent impurities); the negative ion mode may be more susceptible to interference from carbon dioxide in the air and acidic impurities in the solvent.

[0027] Positive ion compounds carry positive charges, and the positive ion mode completes analysis by detecting positively charged ions; negative ion compounds carry negative charges, and the negative ion mode completes analysis by detecting negatively charged ions. The positive ion mode and the negative ion mode have the following main differences: 1. Because basic or neutral basic compounds are more easily ionized in the positive ion mode, the positive ion mode is more suitable for detecting basic or neutral basic compounds, such as amino acids (such as leucine, lysine), amines (such as choline, histamine), and some lipids (such as phosphatidylcholine, triglycerides); 2. Because acidic or neutral acidic compounds are more easily ionized in the negative ion mode, the negative ion mode is more suitable for detecting acidic or neutral acidic compounds, 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).

[0028] Currently, the methods for diagnosing brain glioma and meningioma mainly include computed tomography (CT), magnetic resonance imaging (MRI), and molecular typing diagnosis. The diagnosis of brain glioma and meningioma has entered the molecular era, but technology popularization, heterogeneity management, and clinical transformation are still bottlenecks that need to be broken through, which is explained as follows: The principle of CT is to form an image with certain gray difference after the computer performs corresponding analog-to-digital conversion according to the difference in X-ray absorption rate of different parts of the body. Glioma will produce corresponding density difference due to its tumor composition, surrounding edema, lesion cystic change or hemorrhage, etc., thus showing an image with different gray levels from black to white, which can be used by the diagnosing doctor to judge the nature of the lesion. CT enhancement technology with intravenous injection of high-density contrast agent is also commonly used in glioma diagnosis. After the contrast agent enters the blood vessels, due to the different degrees of destruction of the blood-brain barrier by the tumor and the different blood supply of the tumor itself and the surrounding normal brain tissue, different enhancement performances can be shown. Compared with plain CT without injection of contrast agent, its diagnostic efficiency is greatly improved.

[0029] The advent of magnetic resonance imaging technology has a milestone significance for the image diagnosis of brain tumors, and its value in glioma diagnosis has been widely recognized and applied. Due to the difference in imaging principle, the resolution of magnetic resonance for normal and pathological tissues is obviously higher than that of CT, and it has the advantage of no radiation damage. The low-grade glioma and the normal brain tissue often have no obvious density difference, and the blood-brain barrier destruction is also not obvious. Therefore, the judgment accuracy of plain CT and enhanced CT is insufficient. MRI scanning not only has higher tissue resolution, but also can roughly judge the tissue composition through multi-sequence examination of different technical principles. Unlike the simple transverse tomography of CT, the magnetic resonance can obtain more anatomical information at any angle. In terms of simple scanning (without contrast agent), the overall judgment performance of MRI on tumors has exceeded that of CT.

[0030] Molecular typing diagnosis is a method based on molecular biology and bioinformatics technology, which classifies and diagnoses diseases by analyzing the molecular characteristics (such as genomics, transcriptomics, proteomics, etc.) of samples. It changes the classification basis of diseases from traditional pathological characteristics to molecular characteristics, which can more accurately analyze the heterogeneity of diseases and provide the basis for individualized diagnosis. Although molecular typing diagnosis is the 'gold standard' for diagnosing brain glioma, it needs to obtain pathological tissues through surgery or biopsy, which has the risk of trauma, and gene sequencing takes time, which may delay the diagnosis decision; at the same time, glioma has spatial heterogeneity, and a single biopsy may miss key molecular markers, resulting in inaccurate typing when diagnosing brain glioma.

[0031] Therefore, the current method for diagnosing and distinguishing brain glioma and meningioma cannot simultaneously meet the requirements of safety, non-invasiveness and high detection accuracy.

[0032] In order to solve the above problems, the present application is based on the negative ion mode in mass spectrometry (Mass Spectrometry, MS), and 6 acidic (or slightly acidic) compounds related to brain glioma and meningioma that can be detected in patient saliva are found. The above-mentioned 6 acidic (or slightly acidic) compounds can assist medical personnel to further diagnose and distinguish brain glioma and meningioma, which has good feasibility, accuracy and is completely non-invasive; at the same time, in order to make the technical solutions of the present application more clear and easy to understand, the molecular formula and structure formula of the 6 negative ion saliva markers involved in the examples are described as follows: 1,2-dioleoyl-sn-glycero-3-phosphate: Molecular formula: C 39 H 73 O8P Structural formula:

[0033] D-arabitol Molecular formula: C5H 12 O5 Structural formula:

[0034] Myristic acid Molecular formula: C 14 H 28 O2 Structural formula:

[0035] alpha-Linolenic acid Molecular formula: C 18 H 30 O2 Structural formula:

[0036] N-acetyl-l-glutamate Molecular formula: C7H 11 NO5 Structural formula:

[0037] Caproic acid Molecular formula: C6H 12 O2 Structural formula:

[0038] The application will be further described in detail below in combination with the accompanying drawings and examples. The following examples are only used to illustrate the application and are not used to limit the scope of the application. The experimental methods not specified in the examples are generally carried out according to the conventional conditions.

[0039] In actual work, in order to evaluate whether the saliva compounds detected in the saliva of patients can be used as a judgment factor for distinguishing brain glioma and meningioma, the application collects samples of brain glioma patients and meningioma patients, carries out metabolome sequencing, and uses bioinformatics to statistically analyze the sequencing data, finds positive ion acidic compounds related to brain glioma and meningioma, integrates the compounds with disease information, and maximally predicts and distinguishes brain glioma patients and meningioma patients.

[0040] The application mainly relates to a saliva acidic marker for distinguishing brain glioma and meningioma, a product and application thereof, and the general idea of the whole scheme is that, for brain glioma patients and meningioma patients: Figure 1As shown, we experimentally detect the saliva samples of brain tumor patients and meningioma patients (for specific experimental methods, refer to Examples 1-3), and detect that 6 compounds have a higher correlation with brain tumors and meningiomas. On this basis, we screen 6 negative ion compounds through the preset experimental method, and input the related quantitative data (expression amount) of the compound into the binary logistic regression equation to obtain the advantage of the patient to be tested. The logarithm y, and then according to the formula Z=exp(y) / {1+exp(y)} to calculate the probability Z of the patient to be tested as a brain tumor. The accuracy of this detection method is more than 99%.

[0041] Example 1: Sample collection Collect 100 cases of brain tumor patients and 95 cases of meningioma patients' saliva samples: The sample sources and inclusion criteria of the brain tumor group are as follows: from Wuhan University Zhongnan Hospital, the inclusion criteria are: 1. Age greater than 18 years old; 2. Diagnosed as brain tumor (diagnosed by imaging, histopathology or molecular pathology); 3. No antibiotic or immunosuppressive treatment within 1 month before the collection of biological samples; 4. The patient or his guardian agrees to participate in the research and signs the informed consent form.

[0042] The exclusion criteria of the brain tumor group are as follows: 1. Combined with other malignant tumors; 2. Combined with serious oral diseases or received oral treatment within the past month; 3. Suffering from serious liver disease and kidney damage or receiving continuous kidney replacement therapy, hemodialysis or peritoneal dialysis; 4. Unable to complete saliva sample collection.

[0043] The sample sources and inclusion criteria of the meningioma group are as follows: from Wuhan University Zhongnan Hospital, the inclusion criteria are: 1. Age greater than 18 years old; 2. Diagnosed as meningioma (diagnosed by imaging or histopathology); 3. No antibiotic or immunosuppressive treatment within 1 month before the collection of biological samples; 4. The patient or his guardian agrees to participate in the research and signs the informed consent form.

[0044] The exclusion criteria of the meningioma group are as follows: 1. Combined with other malignant tumors; 2. Combined with serious oral diseases or received oral treatment within the past month; 3. Suffering from serious systemic disease and active infection; 4. Unable to complete saliva sample collection.

[0045] Table 1 Sample information table

[0046] Example 2: Sample extraction The saliva of the patient to be tested is collected according to the standard technique of Navazesh (1993). The subject is asked to rinse the mouth thoroughly with purified water 30 minutes before the saliva sample is collected. The subject should be seated comfortably, with eyes open, head slightly forward, and rest for 5 minutes, and try to reduce the oral and facial movements. The saliva is accumulated in the floor of the mouth, and every 60 seconds the saliva is spat into a collection test tube. Mix well, and if turbid, centrifuge at 3000g for 10 minutes at 4°C, take the supernatant, and about 100ul of each tube is aliquoted into a sterile centrifuge tube for metabolomics. Freeze in liquid nitrogen for 5-10 minutes, and store in a -80°C refrigerator for analysis.

[0047] Example 3: Data statistics and analysis 3.1, Experimental method S1: After the sample is slowly thawed at 4°C, an appropriate amount of sample is added to a pre-cooled methanol / acetonitrile / water solution (2:2:1, v / v), vortex mixed, ultrasonicated at low temperature for 30 minutes, placed at -20°C for 10 minutes, centrifuged at 14000g at 4°C for 20 minutes, and the supernatant is vacuum dried. When mass spectrometry analysis is performed, 100 μL of acetonitrile water solution (acetonitrile:water = 1:1, v / v) is added for reconstitution, vortexed, centrifuged at 14000g at 4°C for 15 minutes, and the supernatant is injected for analysis.

[0048] S2: The sample is separated using a Vanquish LC ultra-high performance liquid chromatography system (UHPLC) HILIC column; the column temperature is 25°C; the flow rate is 0.3 mL / min; the injection volume is 2 μL; the mobile phase composition is A: water + 25 mM ammonium acetate + 25 mM ammonia water, B: acetonitrile; the gradient elution program is as follows: 0---1.5 min, 98% B; 1.5---12 min, B linearly changes from 98% to 2%; 12---14 min, B is maintained at 2%; 14---14.1 min, B linearly changes from 2% to 98%; 14.1--17 min, B is maintained at 98%; during the entire analysis process, the sample is placed in a 4°C autosampler. To avoid the influence of fluctuations in the instrument detection signal, random order is used for continuous analysis of the samples. QC (Quality Control) samples are inserted in the sample queue to monitor and evaluate the stability of the system and the reliability of the experimental data. The QC samples are mixed with all the detected samples in equal amounts, and the consistency of the QC samples is used to judge the stability of the instrument in sample detection.

[0049] S3: The sample was collected by Q Exactive mass spectrometer for primary and secondary spectrum. After the sample was separated by Vanquish LC ultra-high performance liquid chromatography system (UHPLC), mass spectrometry was performed by Q Exactive series mass spectrometer (Thermo). Electrospray ionization (ESI) was used for detection in negative ion mode. The ESI source and mass spectrometry setting parameters are as follows: auxiliary heating gas 1 (Gas1): 60, auxiliary heating gas 2 (Gas2): 60, curtain gas (CUR): 30 psi, ion source temperature: 600℃, spray voltage (ISVF): ±5500 V (positive and negative modes); primary mass-to-charge ratio detection range: 80-1200 Da, resolution: 60000, scan accumulation time: 100ms, secondary using segmented acquisition method, scan range is 70-1200 Da, secondary resolution: 30000, scan accumulation time: 50ms, dynamic exclusion time: 4s.

[0050] S4: The original data is converted into.mzXML format by ProteoWizard, and then peak alignment, retention time correction and peak area extraction are performed by XCMS software. The data extracted by XCMS is first identified for compound structure, data preprocessing, then experimental data quality evaluation, and finally data analysis.

[0051] 3.2, verification result statistics table The relevant statistical data of the validation set markers are shown in Tables 2 and 3. In Table 2, the mean and standard deviation are the expression data of the test compound, the mean determines the center position of the data distribution, and the standard deviation reflects the dispersion degree of the data relative to the mean. The p value is calculated using the formula for rank sum test. The lower the p value, the greater the difference between the brain glioma group and the meningioma group, and the more accurate the detection statistical result.

[0052] Table 2- Relevant statistical data of negative ion markers in the validation set

[0053] Table 3- Relevant expression data of markers in the validation set

[0054]

[0055]

[0056] In actual work, the quantification of compounds is usually relative quantification, whose unit is relative intensity or peak area, rather than specific physical unit. In metabolomics analysis, the expression of compounds is usually represented by peak area, and these quantitative values are usually relative, used to compare the relative content of compounds in different samples.

[0057] From the data of Table 2-Table 3, it can be seen that the six negative ion saliva compounds of the application as detection markers have high accuracy and can be used to distinguish which one of brain glioma and meningioma the patient to be tested (intracranial tumor) is, and are completely non-invasive. At the same time, the six negative ion saliva markers can also be used as target microorganisms for the development of these systems, filling the gap in this field.

[0058] Example 4: Data analysis 4.1 OPLS-DA analysis to screen saliva markers Orthogonal partial least squares discriminant analysis (OPLS-DA) showed that there were significant differences in metabolic profiles between the brain glioma group and the meningioma group. In this study, the screening criteria for significant difference compounds were log2 (FC) > 1, OPLS-DA VIP > 1 and P value < 0.05. Six significant difference compounds in negative ion mode were screened in brain glioma patients and meningioma patients, including: 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-l-glutamate, D-arabitol, myristic acid, alpha-linolenic acid and caproic acid.

[0059] 4.2 Rank sum test According to the saliva markers mined by OPLS-DA analysis, the rank sum test was performed, as shown in Table 4, it was found that the six compounds in negative ion mode were significantly different between the brain glioma group and the meningioma group. Figure 2 P<0.05 represents; P<0.01 represents; P<0.001 represents. P is the significance level (p value).

[0060] 4.3 Establishment of logistic regression model Through the saliva markers mined by 80% of the data, the logistic regression algorithm was used to construct the logistic regression formula of the training model for the six compounds in negative ion mode by using Rstudio software (referred to as R software): y= +​ x x1+ x x2+ x x3+ x x4 x x5 x x6; wherein, y is the dominant logarithm of the patient to be tested; Further, the health probability of the patient to be tested is calculated, which is shown as follows: Z = exp(y) / {1 + exp(y)} wherein, Z is the probability of the patient to be tested being a brain tumor patient, exp(y) is the natural exponential function of y, x1-x7 are the expression amounts of the six compounds, and specifically, x1 is the expression amount of 1,2-dioleoyl-sn-glycero-3-phosphate; x2 is the expression amount of D-arabitol; x3 is the expression amount of Myristic acid; x4 is the expression amount of alpha-Linolenic acid; x5 is the expression amount of N-acetyl-l-glutamate; and x6 is the expression amount of Caproic acid.

[0061] 4.3, ROC verification result Based on the data in Table 3 above, a receiver operating characteristic curve (ROC curve) analysis was performed to obtain the cutoff value (optimal cutoff value).

[0062] The specificity and sensitivity calculation and ROC curve drawing were completed by using Rstudio software. The threshold value of the actual measurement value was calculated first, and then the true positive number (TP), false positive number (FP), true negative number (TN), and false negative number (FN) corresponding to the threshold value were calculated. The specificity (true negative rate) = TN / (TN+FP), and the sensitivity (true positive rate) = TP / (TP+FN). The ROC curve was constructed by 1-specificity and sensitivity, and the integral of the ROC curve was the Area Under Curve (AUC).

[0063] In order to calculate the specificity and sensitivity of a certain index, the Youden coefficient (Youden index = sensitivity + specificity - 1) was calculated first. The specificity and sensitivity corresponding to the maximum Youden coefficient were the specificity and sensitivity of the certain index.

[0064] The expression values of the single acidic markers are directly subjected to receiver operating characteristic curve (ROC curve) analysis to obtain the cutoff value (optimal cutoff value). The ROC curve of the prediction score is shown in Figure 3 .

[0065] The AUC, optimal cutoff value, sensitivity, and specificity of the mimic marker and each single compound prediction score method are shown in Table 4.

[0066] Table 4. Results of negative ion mode ROC diagnostic curve

[0067] In the above method, the present application newly discovers that six negative ion saliva markers are highly related to brain glioma and meningioma, and the expression amounts of each negative ion saliva marker in brain glioma and meningioma are different. The present application finds, by verifying the set data and combining the ROC curve (as shown in Figure 3 ), that the above six negative ion saliva markers have high specificity and sensitivity as analysis and detection variables. Therefore, the above six negative ion saliva markers can be used as detection markers for distinguishing brain glioma patients and meningioma patients. One or more of the six compounds can be used as detection markers for distinguishing brain glioma and meningioma, which is completely non-invasive and has high accuracy.

[0068] Further research finds that, as shown in Figure 1 , among the above six negative ion compounds, four metabolites show a significant increasing trend in brain glioma patients, including 1,2-dioleoyl-sn-glycero-3-phosphate, arabitol, myristic acid, and linolenic acid; two metabolites show a significant increasing trend in meningioma patients, including N-acetyl-L-glutamic acid and hexanoic acid.

[0069] As described in Figure 1 , Figure 2 , Table 2, and Table 3, if a patient to be tested cannot be definitely diagnosed and distinguished between meningioma and brain glioma through imaging detection, medical personnel can detect the saliva compounds (metabolites) of the patient to be tested and diagnose the patient as having meningioma or brain glioma in the following manner: (1) If the patient to be tested is found to have a high content (even a significant increasing trend) of one or more of 1,2-dioleoyl-sn-glycero-3-phosphate, arabitol, myristic acid, and linolenic acid in the saliva after continuous observation for a plurality of time periods, the patient is more likely to be a brain glioma patient; if the patient to be tested is found to have a high content (even a significant increasing trend) of one or more of N-acetyl-L-glutamic acid and hexanoic acid in the saliva, the patient is more likely to be a meningioma patient.

[0070] (2) If, after continuous observation over multiple time periods, the six acidic compounds in the patient's saliva do not exhibit the pattern described in (1) above, then medical staff can combine... Figure 1 Based on the relevant statistical data in Table 2 and the detected expression levels of metabolites in the patients to be tested, a preliminary judgment can be made regarding the specific disease of the patient. In actual practice, if medical staff want to more accurately determine whether the patient has a meningioma or a glioma, they can calculate the type of meningioma or glioma based on the relevant expression levels of the six negative ion salivary markers and referring to the technical solutions described in Examples 5 and 6. (See Example 6 for details).

[0071] It should be noted that the patient to be tested in this embodiment refers to a patient who has been preliminarily diagnosed by medical staff as having a glioma or meningioma after imaging examination. This embodiment is intended to help medical staff further distinguish and confirm whether the patient to be tested has a glioma or a meningioma, especially when the patient is unwilling to undergo invasive biopsy (invasive biopsy carries the risk of surgical trauma and neurological damage).

[0072] Example 5 Based on the above embodiments, this embodiment provides a computer program product, which is used to execute a method for determining whether a patient to be tested is a glioma patient or a meningioma patient, including the following steps: 1) Obtain the expression levels of a single negative ion salivary marker in the patient to be tested; wherein, the single negative ion salivary marker includes 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-L-glutamate, D-arabitol, myristicacid, alpha-linolenic acid, and caproic acid; 2) Substitute the expression level of each individual negative ion salivary biomarker into the binary logistic regression equation to calculate the logarithm y of the patient's dominance; y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6; Wherein, y is the dominant logarithm of the patient to be tested; A is the intercept term, B1-B6 is the regression coefficient of the independent variable; x1-x6 is the expression amount of six compounds, specifically x1 is the expression amount of 1,2-dioleoyl-sn-glycero-3-phosphate; x2 is the expression amount of D-arabitol; x3 is the expression amount of Myristic acid; x4 is the expression amount of alpha-Linolenic acid; x5 is the expression amount of N-acetyl-l-glutamate; x6 is the expression amount of Caproic acid.

[0073] Further, the application determines the parameter values of A, B1-B7 by analyzing the data in Table 3, specifically: A is B1 is B2 is B3 is B4 is B5 is B6 is .

[0074] Therefore, the calculation formula of the rearranged dominant logarithm y is: y= + ×x1+ ×x2+ ×x3+ ×x4 ×x5 ×x6; 3) Calculate the probability Z of the patient to be tested as a brain glioma patient according to y, Z=exp(y) / {1+exp(y)}; wherein, exp(y) is the exponential function of y; 4) According to the comparison of the probability Z and the reference value, determine which one of the brain glioma patient and the meningioma patient the patient to be tested is.

[0075] In actual work, when the Z value is greater than 0.5, it indicates that the patient to be tested has a higher probability of suffering from brain glioma; when the Z value is less than 0.5, it indicates that the patient to be tested has a higher probability of suffering from meningioma; when the Z value is 0.5, it indicates that the patient to be tested may be a meningioma patient or a brain glioma patient, at this time, further detection is required by using other means, such as blood routine, judgment of physical signs, etc. Further, the closer the Z value is to 0.5, the more detection is required by using other means.

[0076] Example 6 Based on the product and method of Example 5, a method for determining which one of a brain glioma patient and a meningioma patient a patient to be tested is provided, and the specific steps are as follows: 1) obtaining the expression amount of each single negative ion saliva marker of the patient to be tested; 2) substituting the expression amount of each single negative ion saliva marker into a binary logistic regression equation to calculate the logarithm y of the advantage of the patient to be tested, the negative ion saliva marker including 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-l-glutamate, D-arabitol, myristic acid, alpha-Linolenic acid and Caproic acid; 3) calculating the probability Z of the patient to be tested as a brain glioma patient according to y, Z=exp(y) / {1+exp(y)}; wherein, exp(y) is the exponential function of y; 4) determining which one of a brain glioma patient and a meningioma patient the patient to be tested is according to the comparison of the probability Z and the reference value.

[0077] In actual work, when the Z value is greater than 0.5, it indicates that the patient to be tested has a higher probability of suffering from brain glioma; when the Z value is less than 0.5, it indicates that the patient to be tested has a higher probability of suffering from meningioma; when the Z value is 0.5, it indicates that the patient to be tested may be a meningioma patient or a brain glioma patient, at this time, further detection by other means is required, and the other means are blood routine, judgment of physical signs, etc. Further, the closer the Z value is to 0.5, the more detection by other means is required.

[0078] Example 7 Based on the above description of Examples 1-5, it can be known that the prediction effect of the markers selected in the present application is good, and medical personnel can use one or several of the six negative ion saliva markers as a detection and diagnosis standard to determine which one of a brain glioma patient and a meningioma patient a patient to be tested is; medical personnel can also combine the six negative ion saliva markers together as markers to detect and diagnose the patient to be tested to determine which one of a brain glioma patient and a meningioma patient the patient to be tested is.

[0079] Therefore, the embodiment also provides a reagent related to brain glioma and meningioma, which can be applied to the preparation of a product related to brain glioma and meningioma to determine which one of brain glioma and meningioma the patient to be tested is; meanwhile, the negative ion saliva marker can be selected from the six negative ion saliva markers discovered in the present application, that is, the negative ion saliva marker in the detection reagent can include one or more of the negative ion saliva markers including 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-l-glutamate, D-arabitol, myristic acid, alpha-Linolenic acid and Caproic acid.

[0080] Embodiment 8 The embodiment also provides a kit which can include the detection reagent described in embodiment 7 to determine which one of brain glioma and meningioma the patient to be tested is; the definition and technical solution of the kit are described in embodiment 7 above, which will not be repeated here. Similarly, the kit described above can also be applied to the preparation of a product for detecting brain glioma, which will not be repeated here.

[0081] Embodiment 9 The present application also provides a product related to brain glioma and meningioma to determine which one of brain glioma and meningioma the patient to be tested is; the product has specificity for one or more of the six negative ion saliva markers discovered in the present application, and the product includes primers, probes, antibodies, aptamers or chips.

[0082] As can be known from the description of embodiments 1-5 and conventional means in the art, in the case of using the newly discovered six negative ion saliva markers as negative ion saliva markers, it should be possible for those skilled in the art to make the corresponding specific products (primers, probes, antibodies, aptamers or chips, etc.), which will not be repeated here.

[0083] Conclusion and explanation: 1. Single compound prediction and differentiation effect: combined with Table 4 and Figure 3 It can be known that the prediction and differentiation effect of 1,2-dioleoyl-sn-glycero-3-phosphate is better, and the prediction and differentiation effect of myristic acid is the second, and the above seven acidic compounds can be used to distinguish which one of brain glioma and meningioma the patient to be tested is.

[0084] 2. Mimic marker prediction effect: combined with Table 4 and Figure 3It can be known that the prediction and distinguishing accuracy of the quasi-state marker (6 compounds combined together) of negative ions is the highest, is more than 99%, and can be used for accurately distinguishing which one of brain glioma and meningioma the to-be-tested patient is.

[0085] 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 scope disclosed by the present application shall be included in the scope of protection of the present application.

Claims

1. A negative ion salivary biomarker associated with gliomas and meningiomas, characterized in that, The negative ion salivary markers include 1,2-dioleoyl-sn-glycero-3-phosphate and / or N-acetyl-L-glutamate.

2. The negative ion saliva marker according to claim 1, characterized in that, The negative ion salivary markers also include one or more of D-arabitol, myristic acid, alpha-linolenic acid, and caproic acid.

3. The negative ion saliva marker according to claim 2, characterized in that, The negative ion salivary markers include 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-L-glutamate, D-arabitol, myristic acid, alpha-linolenic acid, and caproic acid.

4. The use of a reagent for detecting the negative ion salivary marker according to any one of claims 1 to 3 in the preparation of products related to glioma and meningioma.

5. A reagent kit, characterized in that: The reagent comprises a detection reagent for detecting the negative ion saliva markers as described in any one of claims 1-3.

6. Use of the kit of claim 5 in the preparation of products related to glioma and meningioma.

7. A product related to glioma and meningioma, characterized in that: The product includes primers, probes, antibodies, aptamers, or chips that are specific to the negative ion saliva markers of any one of claims 1 to 3.

8. A computer program product, characterized in that: The computer program product is used to perform a method for determining whether a patient under test has a glioma or a meningioma, including the following steps: To obtain the expression levels of a single negative ion salivary biomarker in the patient under test; The expression levels of each individual negative ion salivary biomarker were substituted into a binary logistic regression equation to calculate the logarithm y of the patient's dominance. The negative ion salivary biomarkers include 1,2-dioleoyl-sn-glycero-3-phosphate, N-acetyl-L-glutamate, D-arabitol, myristic acid, alpha-linolenic acid, and caproicacid. The probability Z of a patient being diagnosed with glioma is calculated based on y, where Z = exp(y) / {1 + exp(y)}; and exp(y) is an exponential function of y. Based on the comparison between probability Z and the reference value, it is determined whether the patient to be tested is a glioma patient or a meningioma patient.

9. The computer program product according to claim 8, characterized in that: 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; x1 is the expression level of 1,2-dioleoyl-sn-glycero-3-phosphate; x2 is the expression level of D-arabitol; x3 is the expression level of myristic acid; x4 is the expression level of alpha-linolenic acid; x5 is the expression level of N-acetyl-L-glutamate; and x6 is the expression level of caproic acid.

10. The computer program product according to claim 9, characterized in that: The A is B1 is B2 is B3 is B4 is B5 is B6 is .

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