Plaque microbial markers associated with brain glioma and meningioma and uses thereof

Abstract

The application discloses a dental plaque microbial marker related to brain glioma and meningioma and an application thereof, and belongs to the field of biological medicine. The dental plaque microbial marker comprises one or more of Prevotella shahii, Capnocytophaga gingivalis, Mycobacterium gordonae, Leptothrix oralis and Ottowia. The application provides a kit comprising detection reagents for detecting the relative abundance of the dental plaque microbial marker. The application provides a product for diagnosing brain glioma and a computer program product related to brain glioma and meningioma. The product provided by the application has good feasibility and accuracy, and can be used as a new idea and approach for distinguishing brain glioma and meningioma.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biological medicine, and particularly relates to a dental plaque microbial 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 to the human body between the two, brain glioma is a tumor originating from brain neuroglia cells, which is mostly malignant tumor and has strong invasiveness; meningioma is a tumor originating from meninges (membrane covering the surface of the brain and spinal cord), which is mostly benign tumor and has clear boundary.

[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) can appear as a lesion with 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] It can be seen that, 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 dental plaque microbial marker related to brain glioma and meningioma and application thereof, which is clear, safe and non-invasive, and can provide a new idea and approach for distinguishing brain glioma and meningioma.

[0007] The technical solutions provided by the present application are as follows:

[0008] In a first aspect, a dental plaque microbial marker associated with brain glioma and meningioma is provided, and the dental plaque microbial marker comprises Prevotella shahii and Selenomonas sputigena.

[0009] In the above technical solution, the dental plaque microbial marker further comprises one or more of Ottowia sp., Capnocytophaga gingivalis, and Leptotrichia buccalis.

[0010] In the above technical solution, the dental plaque microbial marker is a marker combination comprising Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis, and Ottowia sp.

[0011] In a second aspect, an application of a reagent for detecting the dental plaque microbial marker of the first aspect in the preparation of a product for distinguishing brain glioma and meningioma is provided.

[0012] In a third aspect, a kit is provided, and the kit contains a reagent for detecting the dental plaque microbial marker of the first aspect.

[0013] In a fourth aspect, an application of the kit of the third aspect in the preparation of a product for distinguishing brain glioma and meningioma is provided.

[0014] 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 dental plaque microbial marker of the first aspect.

[0015] In a sixth aspect, a computer program product associated with brain glioma and meningioma is provided, and the computer program product is used to perform a method of diagnosing whether a patient to be tested has a risk of brain glioma, and the method comprises:

[0016] obtaining a relative abundance value of each single species in dental plaque of the patient to be tested;

[0017] The relative abundance values of each single strain, including Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp., are simultaneously substituted into the same binary logistic regression equation to calculate the logarithm y of the advantage of the patient to be tested.

[0018] The disease probability Z of the patient to be tested being a meningioma patient is calculated according to y, Z = exp (y) / {1 + exp (y)}, wherein exp (y) is an exponential function of y.

[0019] According to the comparison result of the disease probability Z and the reference value, it is judged which one of the glioma patient and the meningioma patient the patient to be tested is.

[0020] In the above technical solution, the formula of the binary logistic regression equation is:

[0021] y = A + B1 x x1 + B2 x x2 + B3 x x3 + B4 x x4 + B5 x x5

[0022] Wherein, A is the intercept term, B1-B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Prevotella shahii, x2 is the relative abundance value of Capnocytophaga gingivalis, x3 is the relative abundance value of Selenomonas sputigena, x4 is the relative abundance value of Leptotrichia buccalis, and x5 is the relative abundance value of Ottowia sp.

[0023] In the above technical solution, A is -1.33, B1 is 61.5, B2 is -18.14, B3 is -48.81, B4 is 30.49, and B5 is 95.51.

[0024] It should be noted that the patient to be tested in the present application is a patient who is preliminarily diagnosed as a glioma or a meningioma by medical staff after imaging detection (or other detection means); the present application aims to be applied to help medical staff further distinguish and diagnose which one of the glioma and the meningioma the patient to be tested is, especially in the case that the patient to be tested is unwilling to undergo invasive biopsy (invasive biopsy has surgical trauma and risk of nerve function damage).

[0025] The beneficial effects of the present application are as follows:

[0026] 1. The present application provides a dental plaque microbial marker associated with brain glioma and meningioma and its application, so as to screen the microorganisms associated with brain glioma and meningioma from the dental plaque 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 dental plaque of the patient by using the single or multiple microorganisms discovered in the present application, so as to determine which one of brain glioma and meningioma the patient to be tested is, which can be used as a new idea and way to distinguish brain glioma and meningioma, and provides a new powerful tool for clinical diagnosis.

[0027] 2. The present application newly discovers five microorganisms which can be detected in the dental plaque of the patient and are associated with brain glioma and meningioma, which include Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.; through experimental analysis and verification (see embodiments 3-5), the above-mentioned five microorganisms 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.

[0028] 3. Further research finds that among the above-mentioned five microbial markers, the workers screen out two microorganisms with higher (even significantly increased) content in the meningioma group, including Capnocytophaga gingivalis and Selenomonas sputigena; at the same time, the workers also screen out three microorganisms with higher (even significantly increased) content in the brain glioma group, including Prevotella shahii, Leptotrichia buccalis and Ottowia sp.

[0029] 4. The present application also provides a reagent and kit, which can use the above-mentioned five microorganisms 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 five dental plaque microorganisms can also be used as target microbial markers for developing these systems, which fills the gap in this field.

[0030] 5. The application also provides a product for distinguishing brain glioma and meningioma, which is based on the relative abundance value of each microorganism, calculates the probability of the patient to be a meningioma patient, and then compares with the reference value, so as to help medical staff to judge which one of brain glioma patient and meningioma patient the patient to be tested is. The product has good feasibility and accuracy, can effectively distinguish brain glioma patients and meningioma patients, and provides a new tool and idea for clinical diagnosis. BRIEF DESCRIPTION OF DRAWINGS

[0031] Fig. 1 is a linear discriminant analysis result graph;

[0032] Fig. 2 is a box scatter plot of dental plaque microbial markers;

[0033] Fig. 3 is a ROC curve. DETAILED DESCRIPTION

[0034] In order to evaluate whether the composition of dental plaque symbiotic flora can be used as a predictor for distinguishing brain glioma and meningioma, the application collects samples of brain glioma patients and meningioma patients, performs metagenomic sequencing, and uses bioinformatics to statistically analyze the sequencing data, finds disease-related dental plaque flora, integrates dental plaque flora and disease information, and maximizes the help of medical staff to distinguish which one of brain glioma patient and meningioma patient the patient to be tested is.

[0035] The application finds five microorganisms with high correlation with brain glioma and meningioma through metagenomic sequencing, including: Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.

[0036] Through further analysis and verification, the above-mentioned five microorganisms are significantly associated with brain glioma and meningioma. Specifically, Capnocytophaga gingivalis and Selenomonas sputigena have higher (even significantly increased) content in the brain glioma group, and Prevotella shahii, Leptotrichia buccalis and Ottowia sp. have higher (even significantly increased) content in the meningioma group.

[0037] The above-mentioned five microorganism markers have high specificity and sensitivity as detection variables through ROC curve analysis, so the five strains can be used as detection markers to diagnose which one of the brain glioma patients and the meningioma patients the patient to be detected is.

[0038] In actual work, the detected Ottowia sp. is a certain strain of Ottowia, and the NCBI taxonomy ID thereof is 1898956 (NCBI Taxonomy ID: 1898956). The Ottowia sp. with the NCBI taxonomy ID of 1898956 is a known strain in the art, and will not be described here.

[0039] 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 are not used to limit the scope of the application. The experimental methods not specified in the examples are generally carried out under conventional conditions.

[0040] Example 1: Sample collection and extraction

[0041] 1.1, Collecting dental plaque samples of 100 brain glioma patients and 100 meningioma patients:

[0042] The sample sources and inclusion criteria of the brain glioma group are as follows: recruited from Zhongnan Hospital of Wuhan University, and the inclusion criteria are as follows: 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 / her guardian agrees to participate in the research and signs the informed consent form.

[0043] 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 the collection of dental plaque samples.

[0044] The sample sources and inclusion criteria of the meningioma group are as follows: recruited from Zhongnan Hospital of Wuhan University, and the inclusion criteria are as follows: 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 / her guardian agrees to participate in the research and signs the informed consent form.

[0045] 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 the collection of dental plaque samples.

[0046] 1.2, Sample extraction

[0047] First, rinse the collection area with sterile saline (rinse the oral cavity for about 10 seconds, repeat 2-3 times), and then use a cotton swab to separate the tooth plaque from the tongue, lips, and damaged areas of the tooth. During collection, scrape the sample 3-4 times in the same area, then flip the swab and continue scraping 3-4 times in the same area. Each sample is collected using a separate swab. After collection, place the swab with plaque in a sample tube containing a preservative solution (leave only 1 / 5 of the protective solution), stir for about 30-40 seconds, shake the tube for 30 seconds, break the swab, and seal the tube. The tooth plaque sample is obtained.

[0048] Example 2: DNA extraction, library construction, and sequencing

[0049] 1. Select the Hi Pure Stool DNA Mini Kit to extract DNA from the collected tooth plaque samples.

[0050] 2. After extraction, use Qubit to detect DNA concentration and 1.5% agarose gel electrophoresis to detect the integrity of the extracted genomic DNA. Select the qualified genomic DNA samples (DNA concentration ≥ 20 ng / μL, volume ≥ 20 μL, total amount ≥ 400 ng) for quality control.

[0051] 3. For the qualified DNA samples, perform random fragmentation, end repair, A base ligation, add adapters and indexes, purify after adapter ligation, and amplify the library. After amplification, detect the DNA concentration (DNA concentration ≥ 40 ng / μL).

[0052] 4. After library detection, pool different libraries according to the effective concentration and target data volume requirements, and then perform sequencing. The macro-genome sequencing platform is Huada T7, and the sequencing strategy is PE150.

[0053] Example 3: LEfSe analysis to screen tooth plaque microbial markers

[0054] 1. Divide the data set

[0055] The KneadData software was used to perform quality control (based on Trimmomatic) and dehosting (based on Bowtie2) on the raw data obtained after the machine. Kraken2 alignment was used to calculate the number of sequences of the species contained in the sample, and Bracken was used to estimate the actual abundance of the species in the sample. 80% of the test personnel (including personnel in the anxiety group and the healthy group) were randomly selected as the training set, and the remaining 20% of the samples were used as the validation set. Then the abundance data of each sample in the training set was analyzed using the LEfSe software, with the default LDA Score filter value of 2.5, and the sample information table is shown in Table 1, as shown in Table 1:

[0056] Table 1 Sample information table

[0057]

[0058] The results are shown in Fig. 1 For the first time, the present application found 5 species related to brain glioma, specifically: the workers screened out 2 microorganisms with higher (even significantly increased) content in the meningioma group, including: Capnocytophaga gingivalis and Selenomonas sputigena; At the same time, the workers screened out 3 microorganisms with higher (even significantly increased) content in the brain glioma group, including: Prevotella shahii, Leptotrichia buccalis and Ottowia sp.

[0059] Example 4: Verify the reliability of the above 5 dental plaque microorganisms

[0060] 1. First, the remaining 20% of the test personnel (including personnel in the brain glioma group and the healthy group) in Example 1 were used as the validation set, and the abundance data of each sample in the validation set was first subjected to binary logistic regression operation, and then subjected to receiver operating characteristic curve (ROC curve) analysis to obtain the cutoff value (optimal cutoff value).

[0061] 2. IBM SPSS Statistics (v27) statistical software was used to calculate specificity and sensitivity and draw ROC curves. The software first calculates the threshold value of the actual measurement value, and then calculates the true positive number (TP), false positive number (FP), true negative number (TN), and false negative number (FN) corresponding to the threshold value.

[0062] Specificity (true negative rate) = TN / (TN+FP),

[0063] Sensitivity (true positive rate) = TP / (TP+FN),

[0064] 3, The ROC curve can be constructed by the specificity and sensitivity, and the integral of the ROC curve is AUC. In order to calculate the specificity and sensitivity of a certain index, the Youden coefficient (Youden index = sensitivity + specificity - 1) is calculated first, and the specificity and sensitivity corresponding to the maximum Youden coefficient are the specificity and sensitivity of the certain index.

[0065] 4, The relative abundance value of the single strain of dental plaque microorganism is 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 Fig. 3 The AUC, optimal cutoff value, sensitivity and specificity of the prediction mimic marker (marker formed by combination of 5 single strains) and single strain are shown in Table 2.

[0066] Table 2 ROC diagnostic curve results

[0067]

[0068] According to Table 2, for single strains, the AUC value of Ottowia sp. is the highest (0.875), and the AUC value of Capnocytophaga gingivalis is the lowest (0.655); the AUC value of the mimic marker (marker formed by combination of 5 single strains) is significantly higher than that of the single strain.

[0069] As can be seen from the above, the 5 strains newly discovered in the application can be used as detection variables, all of which have high specificity and sensitivity, and the AUC of the 5 dental plaque microorganism markers is greater than 65%, therefore, one or more of the 5 dental plaque microorganism markers can be used as a detection marker for the diagnosis of meningioma patients.

[0070] Example 5: Establishment of logistic regression model

[0071] Based on the above screened dental plaque microorganism markers and the relative abundance value of each metabolic marker, the binary logistic regression algorithm in the sp.SS software is used to calculate the first probability of each sample (i.e. the logarithm of the advantage of the patient to be tested y), and then the disease probability optimization formula Z = exp (y) / {1 + exp (y)} is used to calculate the disease probability Z of the sample to be tested. Finally, the disease probability Z is compared with the actual disease condition (such as severity) of each sample, so as to verify the accuracy of the disease probability calculation equation, specifically:

[0072] a. Model establishment

[0073] Based on the proportion of brain glioma population and patient population in the training set, the relative abundance values of the five detected bacteria species are further used as single variables to discuss the linear relationship between the relative abundance values of the five single bacteria and the probability of the sample suffering from the disease. The odds ratio of the to-be-tested patient y (also referred to as the first probability value y) is calculated by a binary logistic regression equation:

[0074] y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5;

[0075] wherein A is an intercept term, B1-B5 are regression coefficients of independent variables; x1 is the relative abundance value of Prevotella shahii, x2 is the relative abundance value of Capnocytophaga gingivalis, x3 is the relative abundance value of Selenomonas sputigena, x4 is the relative abundance value of Leptotrichia buccalis, and x5 is the relative abundance value of Ottowia sp.

[0076] b. Determining the values of A and B1-B5

[0077] After statistical analysis of the sample data, the values of A and B1-B5 are as follows: A is -1.33, B1 is 61.5, B2 is -18.14, B3 is -48.81, B4 is 30.49, and B5 is 95.51.

[0078] At this time, after sorting, the calculation formula of the odds ratio y is:

[0079]

[0080] c. Calculating the probability Z of the to-be-tested patient suffering from the disease

[0081] The first probability value y is substituted into the following formula to calculate the probability Z of the to-be-tested patient being a patient: Z=exp(y) / {1+exp(y)}; wherein Z is the probability value of the to-be-tested patient being a patient, and exp(y) is the natural exponential function of the first probability value y.

[0082] d. Verification set data calculation and statistical analysis

[0083] Based on the data of the verification set, the relative abundance of each single bacteria species of each sample in the disease group and the healthy group is obtained, and then the first probability value y is obtained by using the aforementioned binary logistic regression method, and the probability Z of the to-be-tested sample being a patient is calculated by the formula. The results are shown in Tables 3 and 4.

[0084] Table 4 is the average relative abundance of each species and the standard deviation, which determines the center position of the data distribution, and the standard deviation reflects the dispersion of the data relative to the mean, and the P value is calculated using the formula of the rank sum test, the lower the P value, the greater the difference between the disease group and the healthy group.

[0085] Table 3 Correlation number of validation set markers

[0086]

[0087]

[0088] Note: E is used to represent the power of 10, for example, 3.08043002803e-05 represents 3.08043002803 x 10 -5 .

[0089] Table 4 Correlation abundance statistical data of validation set markers

[0090]

[0091] Note: In Table 4, the mean refers to the average relative abundance, and the standard deviation is the same.

[0092] e. Results and analysis

[0093] According to the results of Example 4 and Example 5, the AUC of the mimic marker (5 species combined) is 1, the best cutoff value is 0.319170805, the sensitivity is 1, and the specificity is 1, so the mimic marker can be applied to distinguish which one of the patient to be tested is a brain glioma patient and a meningioma patient, which is completely non-invasive, has better prediction effect, and has higher accuracy.

[0094] According to the results obtained by combining the description of Table 3 above and the calculation formula of the disease probability Z, the calculation formula of the disease probability of the test sample summarized in the present application is basically correct, which distinguishes which one of the patient to be tested is a brain glioma patient and a meningioma patient; there are cases in Table 3 above that do not fully meet the diagnostic criteria, the reason is that the test patient's dental plaque sample may have false positive results or false negative results, and further detection is required using other means, including blood routine, diagnosis of physical signs, etc.

[0095] Further research found that, for example, Fig. 1Among the five microorganisms discovered in the present application, Capnocytophaga gingivalis and Selenomonas sputigena are higher (even significantly increased) in the meningioma group, and Prevotella shahii, Leptotrichia buccalis and Ottowia sp are higher (even significantly increased) in the glioma group.

[0096] If it is difficult to diagnose a certain to-be-tested patient, and it is difficult to distinguish whether the patient is suffering from meningioma or glioma, the medical staff can detect the dental plaque of the to-be-tested patient, and combine the above Figs. 1-3 and the content recorded in Tables 2-4, to predict which one of meningioma and glioma the patient is suffering from by the following method:

[0097] (1) If the to-be-tested patient is found to have higher content of Capnocytophaga gingivalis and Selenomonas sputigena (compared with the mean and standard deviation of the glioma group in Table 4) in the dental plaque after continuous observation for multiple time periods, even showing a significant increasing trend, the patient is more likely to be a meningioma patient.

[0098] (2) If the to-be-tested patient is found to have higher content of one or more of Prevotella shahii, Leptotrichia buccalis and Ottowia sp (compared with the mean and standard deviation of the meningioma group in Table 4) in the dental plaque after continuous observation for multiple time periods, the patient is more likely to be a glioma patient.

[0099] (3) If the to-be-tested patient is found not to have the above-mentioned (1) and (2) after continuous observation for multiple time periods, the medical staff can make a tendency judgment on which specific disease the patient is suffering from by combining Fig. 1 and Table 4;

[0100] (4) If the medical staff wants to more accurately determine which one of meningioma and glioma the to-be-tested patient is suffering from, the relative abundance values of the five single bacterial species can be used to calculate which one of meningioma and glioma the to-be-tested patient is suffering from according to the technical solutions recorded in Embodiments 5-6. (See the description of Embodiments 6-7 for details).

[0101] It should be noted that the patient to be tested in the embodiment refers to a patient preliminarily diagnosed as brain glioma or meningioma by medical staff through imaging detection, and the embodiment is intended to be applied to help medical staff further distinguish and diagnose which one 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 surgical trauma and risk of nerve function damage).

[0102] Embodiment 6: Computer program product related to brain glioma and meningioma

[0103] Based on the above embodiment, the embodiment provides a computer program product related to brain glioma and meningioma, which is used to execute a method for diagnosing whether a patient to be tested has a risk of brain glioma, comprising the following steps:

[0104] S1, obtaining the relative abundance value of each single species in the dental plaque of the patient to be tested; the single species includes Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.;

[0105] S2, simultaneously inputting the relative abundance value of each single species into the same binary logistic regression equation to calculate the logarithm of the advantage y of the patient to be tested (i.e. the first probability value y);

[0106] ;

[0107] Wherein, x1 is the relative abundance value of Prevotella shahii, x2 is the relative abundance value of Capnocytophaga gingivalis, x3 is the relative abundance value of Selenomonas sputigena, x4 is the relative abundance value of Leptotrichia buccalis, and x5 is the relative abundance value of Ottowia sp.

[0108] S3, calculating the probability Z of the patient to be tested being a meningioma patient according to y, Z=exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y;

[0109] After sorting, the probability Z of the patient to be tested being a meningioma patient can also be expressed as:

[0110]

[0111] S4, judging which one of the brain glioma patient and the meningioma patient the patient to be tested is according to comparison of the probability Z value of the patient to be tested with the reference value.

[0112] In actual work, when the Z value is greater than 0.5, it indicates that the patient to be tested has a greater probability of suffering from meningioma; when the Z value is less than 0.5, it indicates that the patient to be tested has a greater probability of suffering from brain glioma; when the Z value is 0.5, it indicates that the patient to be tested can be a meningioma patient or a brain glioma patient, at this time, further detection by other means is required, the other means being blood routine, judgment of physical signs, and the like. Further, the closer the Z value is to 0.5, the more other means needs to be used for detection.

[0113] Example 7: distinguishing brain glioma patients and meningioma patients

[0114] Based on the product and method of Example 6, the present example also provides a method for judging which one of the brain glioma patient and the meningioma patient the patient to be tested is, the specific steps being as follows:

[0115] S1, collecting a dental plaque sample of a patient to be tested (see Reference Examples 1-3) and detecting the relative abundance value of each single strain in the dental plaque; wherein the single strain includes Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis, and Ottowia sp.

[0116] S2, calculating the advantage of the patient to be tested according to the binary logistic regression equation y;

[0117] ;

[0118] x1 is the relative abundance value of Prevotella shahii, x2 is the relative abundance value of Capnocytophaga gingivalis, x3 is the relative abundance value of Selenomonas sputigena, x4 is the relative abundance value of Leptotrichia buccalis, and x5 is the relative abundance value of Ottowia sp.

[0119] S3, calculating the probability Z of the patient to be tested suffering from meningioma according to y, Z = exp (y) / {1 + exp (y)}; exp (y) is the natural exponential function of y;

[0120] The probability of disease Z can also be expressed as:

[0121]

[0122] S4, judging which one of the brain glioma patient and the meningioma patient the patient to be tested is according to the comparison result of the probability of disease Z and the reference value.

[0123] In actual work, when the Z value is greater than 0.5, it indicates that the patient to be tested has a greater probability of having meningioma; when the Z value is less than 0.5, it indicates that the patient to be tested has a greater probability of having brain glioma; when the Z value is 0.5, it indicates that the patient to be tested can be a meningioma patient or a brain glioma patient, at this time, further detection needs to be performed by using other means, such as blood routine, judgment of physical signs, and the like. Further, the closer the Z value is to 0.5, the more other means need to be used for detection.

[0124] It should be noted that the patient to be tested in the embodiment refers to a patient who is preliminarily diagnosed as having brain glioma or meningioma by medical staff through imaging detection, and the embodiment aims to be applied to help the medical staff further distinguish and diagnose which one of brain glioma and meningioma the patient to be tested has, especially in the case where the patient is unwilling to perform invasive biopsy (invasive biopsy has surgical trauma and risk of nerve function damage).

[0125] Embodiment 8: detection reagent

[0126] Based on the description of the above embodiments 1-5, it can be known that the prediction effect of the marker selected by the present application is good, and medical staff can use a single strain as a marker to detect and diagnose the test sample alone to judge which one of the brain glioma patient and the meningioma patient the patient to be tested is; medical staff can also combine multiple single strains together as a marker to detect and diagnose the test sample to more accurately judge which one of the brain glioma patient and the meningioma patient the patient to be tested is.

[0127] Therefore, the embodiment also provides a reagent for detecting dental plaque microbial markers, which can be applied in the preparation of a product for distinguishing brain glioma and meningioma to diagnose which one of the brain glioma patient and the meningioma patient the patient to be tested is.

[0128] Meanwhile, the dental plaque microbial marker according to the present application can be selected from the 5 single bacterial species newly discovered by the present application and related to brain glioma and meningioma, that is, the dental plaque microbial marker in the detection reagent can be selected from one or more of Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.

[0129] Embodiment 9: Kit

[0130] The present embodiment also provides a kit, which can comprise the detection reagent described in Embodiment 7 to determine which one of brain glioma and meningioma the patient to be tested is; the kit is limited and the technical solution thereof according to the description of Embodiment 7 above, which is not repeated here. Similarly, the above-mentioned kit can also be applied in the preparation of a product for distinguishing brain glioma and meningioma, which is not repeated here.

[0131] Embodiment 10: Product for distinguishing brain glioma and meningioma

[0132] The present application also provides a product for diagnosing which one of brain glioma and meningioma the patient to be tested is; the product is specific to one or more of the 5 single bacterial species newly discovered by the present application and related to brain glioma and meningioma, and the product comprises primers, probes, antibodies, aptamers or chips.

[0133] 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 5 single bacterial species as dental plaque microbial markers, it should be achievable for those skilled in the art to make the corresponding specific products (primers, probes, antibodies, aptamers or chips, etc.), which is not repeated here.

[0134] Conclusion and explanation:

[0135] 1. From Figs. 1-3 As can be known from Table 2, any one of the 5 single bacterial species newly discovered by the present application can be used as a microbial marker for distinguishing brain glioma and meningioma, each single bacterial species has sensitivity and specificity for brain glioma and meningioma, so the dental plaque microbial marker can be selected from any one or several of the 5 single bacterial species newly discovered by the present application, specifically:

[0136] The dental plaque microbial markers associated with brain glioma and meningioma can be selected from one or more of Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.

[0137] 2、From Table 3, it can be seen that when the dental plaque samples are detected, only one or several species are detected, which is a normal phenomenon, because individuals have differences. The disease probability Z of the present application is calculated, so even if a sample contains only a single species, the present application can calculate the probability of the sample (the patient to be tested) having meningioma, thereby helping medical staff to distinguish between brain glioma patients and meningioma patients.

[0138] 3、Prediction effect: for a single species, the AUC value of Ottowia sp. is the highest (0.875), and the AUC value of Capnocytophaga gingivalis is 0.75; the AUC value of the mimic marker (a marker formed by the combination of 5 single species) is significantly higher than that of the single species.

[0139] At the same time, the 5 new species discovered in the present application can all be used as detection variables, and all have high specificity and sensitivity, and the AUC of the 5 dental plaque microbial markers is greater than 65%, and one or more of the 5 dental plaque microbial markers can be used as a basis for determining which one of brain glioma and meningioma the patient to be tested has.

[0140] The above is only a preferred specific embodiment 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 protection scope of the present application.

Claims

1. A dental plaque microbial marker associated with brain glioma and meningioma, characterized in that, The plaque microorganism markers comprise Prevotella shahii, Selenomonas sputigena, Ottowia sp., Capnocytophaga gingivalis and Leptotrichia buccalis.

2. The plaque microorganism marker of claim 1, wherein, The plaque microorganism markers are a marker combination comprising Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.

3. Use of a reagent for detecting the plaque microorganism markers of any one of claims 1 to 2 in the preparation of a product for differentiating brain glioma and meningioma.

4. A kit characterized in that: The kit contains reagents for detecting the plaque microorganism markers of any one of claims 1 to 2.

5. Use of the kit of claim 4 in the preparation of a product for differentiating brain glioma and meningioma.

6. A product for the diagnosis of brain gliomas, characterized in that: The product comprises primers, probes, antibodies, aptamers or chips specific to the plaque microorganism markers of any one of claims 1 to 2.

7. A computer program product associated with a brain glioma and meningioma, characterized by: The computer program product is used to perform a method for diagnosing the risk of a patient under test suffering from brain glioma, the method comprising: obtaining the relative abundance value of each single species in the plaque of the patient under test; simultaneously inputting the relative abundance value of each single species into the same binary logistic regression equation to calculate the log of the odds y of the patient under test, the single species comprising Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.; calculating the probability Z of the patient under test being a meningioma patient according to y, Z = exp (y) / {1 + exp (y)}, wherein exp (y) is the exponential function of y; comparing the probability Z with a reference value to determine which one of a brain glioma patient and a meningioma patient the patient under test is; 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 are the regression coefficients of independent variables; x1 is the relative abundance value of Prevotella shahii, x2 is the relative abundance value of Capnocytophaga gingivalis, x3 is the relative abundance value of Selenomonas sputigena, x4 is the relative abundance value of Leptotrichia buccalis, and x5 is the relative abundance value of Ottowia sp.

8. The product of claim 7, wherein: The A is -1.33, B1 is 61.5, B2 is -18.14, B3 is -48.81, B4 is 30.49, and B5 is 95.51.

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