Positive ion saliva marker related to brain glioma and meningioma and application of positive ion saliva marker

Abstract

The invention discloses a positive ion saliva marker related to brain glioma and meningioma and application of the positive ion saliva marker, and relates to the field of biological medicine. The positive ion saliva marker is prepared from one or more of N1, N12-diacetyl spermine, N-capryloyl dihydrosphingosine and perindopril. The invention provides a kit which is used for detecting positive 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 positive 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 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 brain nerve function damage) during detection, so it is necessary to improve it. SUMMARY

[0006] In view of the above technical problems, the present application provides a positive ion saliva marker related to brain glioma and meningioma and application thereof based on the positive 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 salivary marker associated with intracranial tumor is provided, and the positive ion salivary marker comprises one or more of N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, and perindopril.

[0008] In the above technical solution, the positive ion salivary marker further comprises N,N-dimethylformamide and / or Kaempferitrin.

[0009] In the above technical solution, the positive ion salivary marker comprises N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide, and Kaempferitrin.

[0010] In a second aspect, an application of a reagent for detecting the positive ion salivary 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 positive ion salivary marker of the first aspect.

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

[0013] In a fifth aspect, an application of the positive ion salivary marker in the kit of the fourth aspect in non-diagnostic brain glioma is provided.

[0014] In a sixth aspect, a product for diagnosing brain glioma is provided, and the product comprises primers, probes, antibodies, aptamers, or chips specific to the positive ion salivary marker of the first aspect.

[0015] In a seventh aspect, a computer program product is provided, and the computer program product is used to execute a method for determining whether a patient to be tested is a brain glioma patient or a meningioma patient, and the method comprises the following steps: Obtaining an expression amount of a single positive ion salivary marker of the patient to be tested; the expression amount of each single positive ion saliva marker, including N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide and kaempferitrin, into a binary logistic regression equation to calculate the logarithm y of the advantage of the patient to be tested; According to y, the probability Z of the patient to be tested being a brain glioma patient is calculated, 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, it is judged which one of the brain glioma patient and the meningioma patient the patient to be tested is.

[0016] In the above technical solution, the formula of the binary logistic regression equation is: y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5; Wherein, A is the intercept term, B1-B6 are the regression coefficients of the independent variables; x1 is the expression amount of kaempferitrin; x2 is the expression amount of N-octanoyldihydrosphingosine; x3 is the expression amount of N,N-dimethylformamide; x4 is the expression amount of N1, N12-diacetylspermine; x5 is the expression amount of perindopril.

[0017] In the above technical solution, the A is 1.40201577006857, B1 is 4.90110641456462 10 -8 , B2 is -1.43931694518752 10 -8 , B3 is 9.14647329413452 10 -9 , B4 is -1.26962392406532 10 -8 , B5 is -3.47715909029926 10-8.

[0018] It should be noted that the patient to be tested in the present application is a patient preliminarily diagnosed as brain glioma or meningioma by medical staff through imaging detection, and the present application aims to be applied to help medical staff further distinguish and diagnose which of brain glioma or meningioma the patient to be tested is in, especially in the case that the patient is unwilling to undergo invasive biopsy (invasive biopsy has the risk of surgical trauma and nerve function damage).

[0019] The beneficial effects of the present application are as follows: 1. The present application provides a positive ion saliva marker related to brain glioma and meningioma and its application, so as to screen out basic (or slightly basic) compounds related to brain glioma and meningioma from the saliva of the patient to be tested, thereby helping medical staff to distinguish which of brain glioma and meningioma the patient to be tested is, which has good feasibility and accuracy, and is safe and non-invasive. Medical staff can detect and diagnose the saliva of the patient by using the single or multiple positive ion saliva markers discovered by the present application, so as to determine which of brain glioma and meningioma the patient to be tested is, and the present application can be used as a new idea and way to distinguish brain glioma and meningioma, and provides a brand-new powerful tool for clinical diagnosis.

[0020] 2. The present application is based on the positive ion mode in mass spectrometry (MS), and 5 basic (or slightly basic) compounds related to brain glioma and meningioma that can be detected in the saliva of the patient are discovered, which include N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide and kaempferitrin; through experimental analysis and verification (see embodiments 3 and 4), the above-mentioned 5 positive ion compounds have high specificity and sensitivity as detection variables, and can be used as detection markers for diagnosing which of brain glioma and meningioma the patient to be tested is.

[0021] 3. Further research revealed that among the five positively charged compounds, two compounds were found in higher concentrations in patients with gliomas: N1,N12-diacetylspermine and N-octanoyldihydrosphingosine; and three compounds were found in higher concentrations in patients with meningiomas: kaempferitrin, N,N-dimethylformamide, and perindopril.

[0022] 4. This invention also provides a reagent and kit that can use the above-mentioned five positively charged compounds as detection markers to distinguish between glioma and meningioma in a patient being tested. This method is completely non-invasive and highly accurate. Furthermore, the five positively charged salivary markers can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0023] 5. This invention also provides a product for distinguishing between gliomas and meningiomas. This product can calculate the probability that a patient has a glioma based on the expression levels of various basic compounds, and then compare this probability with reference values, thereby helping medical personnel determine whether the patient has a glioma or a meningioma. This product has good feasibility and accuracy, and can effectively distinguish between glioma and meningioma patients, providing a new tool and approach for clinical diagnosis. Attached Figure Description Figure 1 The graph shows the fold change analysis results of the expression levels of biomarkers with significant differences in the positive ion mode. Figure 2 Box plot of significance for positive ion mode differential markers; Figure 3 This is the ROC diagnostic curve. Detailed Implementation

[0024] 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. Its core is to convert sample molecules into gaseous ions and then separate and detect them based on their mass differences.

[0025] Positive ion mode and negative ion mode are two core and selectable detection methods in mass spectrometry (MS). The detection mode (positive ion mode or negative ion mode) selected by the operator directly affects the sensitivity and accuracy of the analysis results.

[0026] The same points of the above-mentioned positive ion mode and negative ion mode include: (1) The pretreatment steps of the sample (such as extraction, centrifugation, filtration, etc. of the saliva sample) are generally consistent whether in the positive ion mode or the negative ion mode. (2) The liquid chromatography (LC) separation conditions (such as chromatographic column, mobile phase, gradient, etc.) used in the two modes are generally the same. (3) Both modes collect data by 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 as many compounds in the sample as possible accurately.

[0027] 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 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.

[0028] 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 to basic compounds are more easily ionized in the positive ion mode, the positive ion mode is more suitable for detecting basic or neutral to 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 to acidic compounds are more easily ionized in the negative ion mode, the negative ion mode is more suitable for detecting acidic or neutral to 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).

[0029] Currently, the main methods for diagnosing brain glioma and meningioma are classified into the following categories: computed tomography (CT), magnetic resonance imaging (MRI), and molecular typing diagnosis. The diagnosis of brain glioma and meningioma has entered the molecular era, but the popularization of technology, heterogeneous management, and clinical transformation are still bottlenecks that need to be broken through, as follows: The principle of CT is to convert the difference in X-ray absorption rate of different parts of the body into a corresponding digital image with certain gray difference after computer conversion. 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 and the blood supply of the tumor itself and the surrounding normal brain tissue, different enhancement performances can be shown. Compared with non-contrast CT, its diagnostic efficiency is greatly improved.

[0030] The emergence of magnetic resonance imaging technology has a milestone significance for the imaging 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 significantly higher than that of CT, and it has the advantage of no radiation damage. There is no obvious density difference between low-grade glioma and normal brain tissue, and the blood-brain barrier is not obviously destroyed, so 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, magnetic resonance can obtain more anatomical information at any angle. In terms of simple plain scanning (without contrast agent), the overall judgment efficiency of MRI on tumors has exceeded that of CT.

[0031] 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 features 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 requires pathological tissue obtained by 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, leading to inaccurate typing when diagnosing brain glioma.

[0032] 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.

[0033] To solve the above problems, the present application finds 5 basic (or alkaline) compounds that can be detected in the saliva of patients and are related to brain glioma and meningioma based on the positive ion mode in mass spectrometry (MS). The 5 basic (or alkaline) compounds can assist medical personnel in further diagnosing and distinguishing brain glioma and meningioma, and have good feasibility, accuracy and are completely non-invasive. In order to make the technical solutions of the present application clearer and easier to understand, the molecular formula and structural formula of the 5 positive ion saliva markers involved in the examples are described as follows: N1,N12-diacetylspermine: Molecular formula: C 14 H 30 N4O2 Structural formula:

[0034] N-octanoyldihydrosphingosine Molecular formula: C 26 H 53 NO3 Structural formula:

[0035] Perindopril Molecular formula: C 19 H 32 N2O5 Structural formula:

[0036] N,N-dimethylformamide (N,N-dimethylformamide) Molecular formula: C3H7NO Structural formula:

[0037] Kaempferitrin Molecular formula: C 27 H 30 O 14 Structural formula:

[0038] The application will be further described in detail below in combination with the drawings and examples. The following examples are only used to illustrate the application and not 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 basic 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 basic marker for distinguishing brain glioma and meningioma, a product and an application thereof. The general idea of the whole scheme is that, for brain glioma patients and meningioma patients: Figure 1 As shown in the formula, we carry out experimental detection on the saliva samples of brain glioma patients and meningioma patients (for specific experimental methods, refer to examples 1-3), detect 5 compounds with higher correlation with brain glioma and meningioma, on the basis of which, we screen out 5 positive ion compounds through a preset experimental method, input the related quantitative data (expression amount) of the compounds into a binary logistic regression equation to obtain the advantage logarithm y of the to-be-detected patient, and then calculate the probability Z of the to-be-detected patient being a brain glioma patient according to the formula Z=exp(y) / {1+exp(y)}. The accuracy of this detection method is more than 99%.

[0041] Example 1: Sample collection 115 cases of brain glioma patients and 95 cases of meningioma patients are collected: The sample sources and inclusion criteria of the brain glioma group are as follows: from the South Central Hospital of Wuhan University, the inclusion criteria are: 1. Age greater than 18 years old; 2. Diagnosed as brain glioma (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 glioma 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 source and inclusion criteria of the meningioma group are as follows: from the recruitment of Wuhan University Zhongnan Hospital, the inclusion criteria are: 1. Age greater than 18 years old; 2. Diagnosed as meningioma (imaging or histopathological diagnosis); 3. No treatment with antibiotics or immunosuppressive agents within 1 month before sampling; 4. The patient or his guardian agrees to participate in the study 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 diseases and active infections; 4. Unable to complete saliva sample collection.

[0045] Table 1 Sample information table

[0046] Example 2: Sample extraction According to the standard technique of Navazesh (1993), saliva of the patient to be tested is collected. The subject should rinse the oral cavity thoroughly with pure water 30 minutes before collecting the saliva sample. The subject should sit comfortably, open his eyes, slightly tilt his head forward, rest for 5 minutes, and try to minimize oral and facial movements. Saliva accumulates at the bottom of the mouth, and saliva is spit into a collection test tube every 60 seconds. Mix well, take samples directly without turbidity, and if turbid, centrifuge at 3000g for 10 minutes at 4°C, take the supernatant, and divide about 100ul into sterile centrifuge tubes for metabolomics. Freeze in liquid nitrogen for 5-10 minutes, store in a -80°C refrigerator, and use for analysis.

[0047] Example 3: Data statistics and analysis 3.1 Experimental method S1: After thawing the sample slowly in a 4°C environment, add an appropriate amount of sample to a pre-cooled methanol / acetonitrile / water solution (2:2:1, v / v), vortex mix, ultrasonic at low temperature for 30 min, stand at -20°C for 10 min, centrifuge at 14000 g at 4°C for 20 min, vacuum dry the supernatant, add 100 μL acetonitrile water solution (acetonitrile: water = 1:1, v / v) for reconstitution when mass spectrometry analysis, vortex, centrifuge at 14000 g at 4°C for 15 min, and take the supernatant for analysis.

[0048] S2: The sample was separated by Vanquish LC ultra-high performance liquid chromatography (UHPLC) HILIC column; column temperature 25℃; flow rate 0.3 mL / min; injection volume 2 μL; mobile phase composition A: water + 25 mM ammonium acetate + 25 mM ammonia, 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℃ automatic injector. In order to avoid the influence of instrument detection signal fluctuation, the continuous analysis of samples was carried out in random order. 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. The QC samples were mixed with all the detected samples in equal amounts, and the consistency of the QC samples was used to judge the stability of the instrument in sample detection.

[0049] S3: The sample was collected by Q Exactive series mass spectrometer for primary and secondary spectrum. After the sample was separated by Vanquish LC ultra-high performance liquid chromatography (UHPLC), mass spectrometry was performed by Q Exactive series mass spectrometer (Thermo), and electrospray ionization (ESI) was used for detection in positive 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: 100 ms, secondary using segmented acquisition method, scan range is 70-1200 Da, secondary resolution: 30000, scan accumulation time: 50 ms, dynamic exclusion time: 4 s.

[0050] S4: The raw data is converted into.mzXML format by ProteoWizard, and then XCMS software is used for peak alignment, retention time correction and extraction of peak area. The data extracted by XCMS is first subjected to compound structure identification, data preprocessing, then experimental data quality evaluation, and finally data analysis.

[0051] 3.2, verification result statistical table The relevant statistical data of the verification 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 of the data relative to the mean. The p value is calculated using the formula for the 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 statistics.

[0052] Table 2 - Relevant statistical data of positive ion markers in the verification set

[0053] Note: e is used to represent the power of 10, for example, 4.48e-12 represents 4.48 x 10 -12 .

[0054] Table 3 - Relevant expression data of markers in the verification set

[0055] In actual work, the quantification of compounds is usually relative quantification, with units of relative intensity or peak area, rather than specific physical units. 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.

[0056] From the data in Tables 2-3, it can be seen that the five positive ion saliva compounds of the present application as detection markers have high accuracy and can be used to distinguish which of the brain glioma and meningioma the patient (intracranial tumor) to be tested is, and are completely non-invasive. At the same time, the five positive ion saliva markers can also be used as target microorganisms for the development of these systems, filling the gap in this field.

[0057] 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 glioma group and the meningioma group. In this study, the log2 (FC) > 1, OPLS-DA VIP > 1 and P value < 0.05 were used as the screening criteria for significant differential compounds. A total of 5 positive ion mode significant differential compounds were screened in the glioma patients and the meningioma patients, including: N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide and kaempferitrin.

[0058] 4.2 Rank sum test According to the OPLS-DA analysis of the mined saliva markers, the rank sum test was performed, as shown in Figure 2 , it was found that the 5 compounds in positive ion mode were significantly different between the meningioma group and the meningioma group. represent P < 0.05; represent P < 0.01; represent P < 0.001. P is the significance level (p value).

[0059] 4.3 Establishment of logistic regression model Based on the 80% data mining saliva markers, the logistic regression algorithm was used to construct the training model of the 5 compounds in positive ion mode by using Rstudio software (referred to as R software): y = 1.40201577006857 + 4.90110641456462 10 -8 × x1-1.43931694518752 10 -8 × x2+9.14647329413452 10 -9 × x3-1.26962392406532 10 -8 × x4-3.47715909029926 10 -8 × x5; Wherein, y is the advantage logarithm of the patient to be tested; Further, the health probability of the patient to be tested was calculated, as shown below: Z = exp (y) / {1 + exp (y)} Wherein, Z is the probability of the patient to be a brain glioma patient, exp(y) is the natural exponential function of y, x1-x5 are the expression amounts of the five compounds; X1 is the expression amount of Kaempferitrin; X2 is the expression amount of N-octanoyl sphingosine; X3 is the expression amount of N,N-dimethylformamide; X4 is the expression amount of N1,N12-diacyl spermine; X5 is the expression amount of Perindopril.

[0060] 4.3, ROC verification results 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).

[0061] The specificity and sensitivity were calculated and the ROC curve was drawn 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), the sensitivity (true positive rate) = TP / (TP+FN), and the ROC curve was constructed by 1-specificity and sensitivity. The integral of the ROC curve is the Area Under Curve (AUC, Area Under Curve).

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

[0063] The expression amount of a single basic marker was 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 The AUC, optimal cutoff value, sensitivity, and specificity of the mimic marker and each single compound prediction score method are shown in Table 4.

[0064] Table 4. Results of positive ion mode ROC diagnostic curve

[0065] In the above method, the present application has found that five positive ion saliva markers are highly related to brain glioma and meningioma, and the expression amounts of each positive ion saliva marker in brain glioma and meningioma are different. The present application verifies the set data and combines the ROC curve (receiver operating characteristic curve) to obtain the AUC (Area Under Curve) of each positive ion saliva marker, and the AUC of each positive ion saliva marker is shown in Table 4. Figure 3), it is found that the above-mentioned 5 positive ion saliva markers have high specificity and sensitivity as analysis detection variables, and therefore the above-mentioned 5 positive ion saliva markers can distinguish the detection markers of brain glioma patients and meningioma patients, and one or more of the 5 compounds as detection markers can be used to distinguish brain glioma and meningioma, completely non-invasive and high accuracy.

[0066] Further research shows that, as shown in Figure 1 , among the above-mentioned 5 positive ion compounds, 2 compounds have higher content (even an increasing trend) in brain glioma patients, including: N1, N12-diacetylspermine, N-octanoyldihydrosphingosine; 3 compounds have higher content (even an increasing trend) in meningioma patients, including: Kaempferitrin, N,N-dimethylformamide and Perindopril.

[0067] From the description of Figure 1 , Figure 2 , Table 2 and Table 3, if a patient to be tested is difficult to be 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 one of meningioma and brain glioma through the following ways: (1) If the patient to be tested is found to have one or more of N1, N12-diacetylspermine and N-octanoyldihydrosphingosine in saliva with higher content (even a significant increasing trend) after continuous observation for multiple periods, the patient has a higher possibility of being a brain glioma patient; if the patient has one or more of Kaempferitrin, N,N-dimethylformamide and Perindopril with higher content (even a significant increasing trend), the patient has a higher possibility of being a meningioma patient.

[0068] (2) If the patient to be tested is found to have no regularity in the 5 basic compounds in saliva after continuous observation for multiple periods, medical personnel can combine Figure 1And according to the content and concentration of the above-mentioned five positive ion saliva markers, the specific disease of the patient is determined; in actual work, if the medical staff wants to more accurately determine which one of the meningioma and the glioma the patient is, the expression data of the five positive ion saliva markers can be used to calculate which one of the meningioma and the glioma the patient is according to the technical solutions described in embodiments 5-6.

[0069] It should be noted that the patient to be measured in this embodiment is a patient who is preliminarily diagnosed as having a glioma or a meningioma by medical staff through imaging detection. This embodiment is intended to be applied to help medical staff further distinguish and diagnose which one of a glioma and a meningioma the patient to be measured is, especially in the case where the patient is unwilling to undergo invasive biopsy (which has the risk of surgical trauma and nerve function damage).

[0070] Embodiment 5 Based on the above-mentioned embodiments, the present embodiment provides a computer program product for executing a method for determining which one of a glioma patient and a meningioma patient the patient to be measured is, comprising the following steps: 1) obtaining the expression amount of a single positive ion saliva marker of the patient to be measured; wherein the single positive ion saliva marker comprises N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide and kaempferitrin; 2) substituting the expression amount of each single positive ion saliva marker into a binary logistic regression equation to calculate the logarithm of the advantage y of the patient to be measured; y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5; wherein y is the logarithm of the advantage of the patient to be measured; A is the intercept term, B1-B5 are the regression coefficients of the independent variables x1-x5, x1-x5 are the expression amounts of the five compounds; X1 is the expression amount of kaempferitrin; X2 is the expression amount of N-octanoyldihydrosphingosine; X3 is the expression amount of N,N-dimethylformamide; X4 is the expression amount of N1, N12-diacetylspermine; and X5 is the expression amount of perindopril.

[0071] Further, the application determines the parameter values of A, B1-B5 by analyzing the data in Table 3, and the specific values are: A is 1.40201577006857, B1 is 4.90110641456462 10 -8 , B2 is -1.43931694518752 10 -8 , B3 is 9.14647329413452 10 -9 , B4 is -1.26962392406532 10 -8 , and B5 is -3.47715909029926 10 -8 .

[0072] Therefore, the calculation formula of the rearranged advantage logarithm y is: y=1.40201577006857+4.90110641456462 10 -8 ×x1-1.43931694518752 10 -8 ×x2+9.14647329413452 10 -9 ×x3-1.26962392406532 10 -8 ×x4-3.47715909029926 10 -8 ×x5; 3) Calculate the probability Z of the patient to be 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 between the probability Z and the reference value, determine which one of the brain glioma patient and the meningioma patient the patient to be.

[0073] In actual work, when the Z value is greater than 0.5, it indicates that the patient to be detected 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 detected has a higher probability of suffering from meningioma; when the Z value is 0.5, it indicates that the patient to be detected 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.

[0074] 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 to-be-tested patient is, is provided, and the specific steps are as follows: 1) obtaining the expression amount of each single positive ion saliva marker of the to-be-tested patient; 2) substituting the expression amount of each single positive ion saliva marker into a binary logistic regression equation to calculate the logarithm y of the advantage of the to-be-tested patient, the positive ion saliva marker including N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide, and kaempferitrin; 3) calculating the probability Z of the to-be-tested patient being 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 to-be-tested patient is according to the comparison of the probability Z and a reference value.

[0075] In actual work, when the Z value is greater than 0.5, it indicates that the to-be-tested patient has a greater probability of suffering from a brain glioma; when the Z value is less than 0.5, it indicates that the to-be-tested patient has a greater probability of suffering from a meningioma; when the Z value is 0.5, it indicates that the to-be-tested person 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, and the like. Further, the closer the Z value is to 0.5, the more detection by other means is required.

[0076] Example 7 Based on the above description of Examples 1-5, it can be known that the prediction effect of the selected markers is good, and medical personnel can use one or several of the five positive ion saliva markers as a detection and diagnosis standard to determine which one of a brain glioma patient and a meningioma patient a to-be-tested patient is; medical personnel can also combine the five positive ion saliva markers together as markers to detect and diagnose a to-be-tested sample to determine which one of a brain glioma patient and a meningioma patient a to-be-tested patient is.

[0077] Therefore, the embodiment also provides a reagent related to brain glioma and meningioma, which can be applied to 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 positive ion saliva marker can be selected from the five positive ion saliva markers discovered in the application, that is, the positive ion saliva marker in the detection reagent can include one or more of N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide and kaempferitrin.

[0078] 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 limitation and technical solution of the kit of the application can refer to the description of embodiment 7 above, and will not be repeated here. Similarly, the kit described above can also be applied to preparation of a product for detecting brain glioma, and will not be repeated here.

[0079] Embodiment 9 The 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 five positive ion saliva markers discovered in the application, and the product includes primers, probes, antibodies, aptamers or chips.

[0080] It can be known from the description of embodiments 1-5 and conventional means in the art that, in the case of using the five newly discovered positive ion saliva markers as positive ion saliva markers, the corresponding product (primers, probes, antibodies, aptamers or chips, etc.) with specificity should be achievable for those skilled in the art, and will not be repeated here.

[0081] Conclusion and explanation: 1. Single compound prediction and differentiation effect: combined with table 4 and Figure 3It can be known that the prediction and differentiation effects of N-octanoyldihydrosphingosine and Kaempferitrin are better, the prediction and differentiation effects of N,N-dimethylformamide and N1,N12-diacetylspermine are second, and the four basic compounds can be used to distinguish which one of brain glioma and meningioma the patient to be tested is.

[0082] 2, mimic marker prediction effect: combined with table 4 and Figure 3 It can be known that the prediction and differentiation accuracy of the mimic marker of positive ions (5 compounds are combined together) is the highest, which is more than 99%, and can be used to accurately distinguish which one of brain glioma and meningioma the patient to be tested is.

[0083] The above is only the preferred specific embodiment of the present application, but the scope of protection of the present application is not limited to this, any modification, equivalent replacement and improvement made by any person skilled in the art within the technical range disclosed by the present application should be included in the protection scope of the present application.

Claims

1. A positive ion salivary marker associated with brain glioma and meningioma, characterized in that, The positive ion saliva marker comprises one or more of N1, N12-diacetylspermine, N-octanoyldihydrosphingosine and perindopril.

2. The positive ion salivary marker of claim 1, wherein, The positive ion saliva marker further comprises N,N-dimethylformamide and / or kaempferitrin.

3. The positive ion salivary marker of claim 2, wherein, The positive ion saliva marker comprises N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide and kaempferitrin.

4. Use of a reagent for detecting the positive ion saliva marker according to any one of claims 1 to 3 in the manufacture of a product associated with brain glioma and meningioma.

5. A kit characterized in that: A detection reagent for detecting the positive ion saliva marker according to any one of claims 1 to 3.

6. Use of the kit according to claim 5 in the manufacture of a product associated with brain glioma and meningioma.

7. A product associated with brain gliomas and meningiomas, characterized in that: The product comprises primers, probes, antibodies, aptamers or chips specific to the positive ion saliva marker according to any one of claims 1 to 3.

8. A computer program product, characterized by: The computer program product is used to perform a method for determining whether a patient to be tested is a brain glioma patient or a meningioma patient, comprising the following steps: Obtaining the expression amount of each single positive ion saliva marker in the patient to be tested; Substituting the expression amount of each single positive ion saliva marker into a binary logistic regression equation to calculate the logarithm of the advantage y of the patient to be tested, wherein the positive ion saliva marker comprises N1, N12-diacetylspermine, N-octanoyldihydrosphingosine, perindopril, N,N-dimethylformamide and kaempferitrin; According to y, the probability Z of the patient to be tested being a brain glioma patient is calculated, Z = exp (y) / {1 + exp (y)}; wherein exp (y) is the exponential function of y; According to the comparison between the probability Z and the reference value, it is determined whether the patient to be tested is a brain glioma patient or a meningioma patient.

9. The computer program product of claim 8, wherein: 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; Wherein, A is the intercept term, B1-B5 is the regression coefficient of independent variable; x1 is the expression of Kaempferitrin; x2 is the expression of N-octanoyldihydrosphingosine; x3 is the expression of N,N-dimethylformamide; x4 is the expression of N1,N12-Diacetylspermine; x5 is the expression of Perindopril.

10. The computer program product of claim 9, wherein: The A is 1.40201577006857, B1 is 4.90110641456462 10 -8 , B2 is -1.43931694518752 10 -8 , B3 is 9.14647329413452 10 -9 , B4 is -1.26962392406532 10 -8 , B5 is -3.47715909029926 10 -8 .

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