Dental plaque microbial marker related to brain glioma and meningioma and application of dental plaque microbial marker

Abstract

The invention discloses a dental plaque microbial marker related to brain glioma and meningioma and application of the dental plaque microbial marker, and belongs to the field of biological medicine. The dental plaque microbial marker is prepared from one or more of prevotella vulgaris, carbon dioxide cytophaga gingivalis, meniscus expectogenes, oral cilia and otobacterium. The invention provides a kit which comprises a detection reagent for detecting the relative abundance of the dental plaque microbial marker. The invention provides a product for diagnosing brain glioma and a computer program product related to the brain glioma and meningioma. The product provided by the invention 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 invention belongs to the field of biomedicine, and in particular relates to dental plaque microbial markers associated with glioma and meningioma and applications thereof. Background Art

[0002] Glioma and meningioma are both subtypes of intracranial tumors, but there are significant differences in the harmfulness of the two to the human body. Glioma is a tumor that originates from the glial cells in the brain. It is mostly malignant and highly invasive; meningioma is a tumor that originates from the meninges (the membrane covering the surface of the brain and spinal cord). It is mostly benign and has clear boundaries.

[0003] In current clinical practice, imaging examinations are the core means of detecting gliomas and meningiomas (such as computed tomography (CT) and magnetic resonance imaging (MRI). However, it is difficult to distinguish gliomas from meningiomas in clinical application, and they have the following significant limitations: 1) Imaging examinations are not sensitive enough to early or small lesions, which can easily lead to missed or delayed diagnosis; 2) Low-grade gliomas (such as grade II astrocytomas) may appear as lesions with clear boundaries and no enhancement, especially when meningiomas are combined with cystic changes or calcifications; 3) Atypical meningiomas (WHO grade II) may show infiltrative growth, heterogeneous enhancement, and surrounding edema, with imaging manifestations similar to high-grade gliomas (such as anaplastic astrocytomas).

[0004] When imaging tests cannot distinguish between gliomas and meningiomas, people often use invasive biopsy (also known as pathological biopsy) to distinguish between gliomas and meningiomas. However, although invasive biopsy is the gold standard for diagnosis, it carries risks such as surgical trauma, neurological damage, and sampling errors.

[0005] From this, we can see that among the existing methods of distinguishing between gliomas and meningiomas, the accuracy of imaging tests needs to be further improved, and invasive biopsy (also known as pathological biopsy) has the risk of surgical trauma (or even the risk of brain nerve damage) during testing. Therefore, it is necessary to improve this. Summary of the Invention

[0006] In response to the above technical problems, the present invention provides a dental plaque microbial marker related to glioma and meningioma and its application, which is clear, safe and non-invasive, and can provide a new idea and approach for distinguishing glioma and meningioma.

[0007] The technical solutions provided by the present invention are as follows: In a first aspect, a dental plaque microbial marker associated with brain glioma and meningioma is provided, wherein the dental plaque microbial marker includes Prevotella shahii and Selenomonas putigena.

[0008] In the above technical solution, the dental plaque microbial markers further include one or more of Ottowia sp., Capnocytophaga gingivalis and Leptotrichia buccalis.

[0009] In the above technical solution, the dental plaque microbial marker is a marker combination consisting of Prevotellashahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.

[0010] In a second aspect, a reagent for detecting the dental plaque microbial marker described in the first aspect is provided for use in preparing a product for distinguishing between glioma and meningioma.

[0011] A third aspect is a kit containing reagents for detecting the dental plaque microbial markers described in the first aspect.

[0012] In a fourth aspect, a method is provided for using the kit described in the third aspect in preparing a product for distinguishing between glioma and meningioma.

[0013] In a fifth aspect, a product for diagnosing brain glioma is provided, wherein the product comprises primers, probes, antibodies, aptamers or chips specific to the dental plaque microbial markers described in the first aspect.

[0014] In a sixth aspect, a computer program product related to glioma and meningioma is provided, wherein the computer program product is used to execute a method for diagnosing whether a patient to be tested has a risk of glioma, the method comprising: Obtain the relative abundance value of each single bacterial species in the dental plaque of the patient to be tested; Substituting the relative abundance values ​​of each single bacterial species into the same binary logistic regression equation simultaneously to calculate the logarithm y of the odds of the patient to be tested, the single bacterial species including Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis, and Ottowia sp.; Calculate the probability Z of the patient being diagnosed with meningioma based on y, Z=exp(y) / {1+exp(y)}, where exp(y) is the exponential function of y; According to the comparison result of the disease probability Z and the reference value, it is determined whether the patient to be tested is a glioma patient or a meningioma patient.

[0015] 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 Among them, A is the intercept term, B1-B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Prevotellashahii, 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.

[0016] 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.

[0017] It should be noted that the patients to be tested referred to in this application are patients who are preliminarily diagnosed with glioma or meningioma by medical staff after imaging examination (or other detection methods); this application is intended to help medical staff further distinguish and confirm whether the patients to be tested have glioma or meningioma, especially when the patients to be tested are unwilling to undergo invasive biopsy (invasive biopsy involves surgical trauma and the risk of neurological damage).

[0018] The beneficial effects of the present invention are as follows: 1. The present invention provides a dental plaque microbial marker associated with gliomas and meningiomas and its application, which can be used to screen microorganisms associated with gliomas and meningiomas from the dental plaque of a patient to be tested, thereby helping medical staff distinguish whether the patient is a glioma patient or a meningioma patient. The method has good feasibility and accuracy, and is safe and non-invasive. Medical staff can use single or multiple microorganisms discovered in this application to detect and diagnose the patient's dental plaque to determine whether the patient is a glioma or a meningioma. This method can serve as a new idea and approach to distinguish between gliomas and meningiomas, providing a new and powerful tool for clinical diagnosis.

[0019] 2. The present invention has discovered five new microorganisms that can be detected in patients' dental plaque and are associated with gliomas and meningiomas. These microorganisms include Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis, and Ottowia sp. Experimental analysis and verification (see Examples 3-5 for details) have shown that these five microorganisms have high specificity and sensitivity as detection variables. These five microorganisms can be used as detection markers to diagnose whether a patient has glioma or meningioma.

[0020] 3. Further research found that among the above five microbial markers, the staff screened out two microorganisms with higher content (even significantly increased) in the meningioma group, including Capnocytophaga gingivalis and Selenomonas sputigena; at the same time, the staff also screened out three microorganisms with higher content (even significantly increased) in the brain glioma group, including Prevotella shahii, Leptotrichia buccalis and Ottowia sp.

[0021] 4. The present invention also provides a reagent and kit that can use the five microorganisms described above as detection markers to distinguish between glioma and meningioma in patients, completely non-invasively and with high accuracy. Furthermore, the five dental plaque microorganisms described above can also serve as target microbial markers for the development of these systems, filling a gap in this field.

[0022] 5. The present invention also provides a product for differentiating between gliomas and meningiomas. Based on the relative abundance of various microorganisms, this product calculates the probability that a patient has a meningioma. This is then compared with a reference value, helping medical staff determine whether the patient has a glioma or a meningioma. This product demonstrates excellent feasibility and accuracy, effectively distinguishing between gliomas and meningiomas, and provides a new tool and approach for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 This is the result graph of linear discriminant analysis; Figure 2 is a box scatter plot of dental plaque microbial markers; Figure 3 is the ROC curve. DETAILED DESCRIPTION

[0023] In order to evaluate whether the composition of the symbiotic flora in dental plaque can be used as a predictive factor to distinguish between glioma and meningioma, the present invention collected samples from patients with glioma and meningioma, performed metagenomic sequencing, and used bioinformatics to perform statistics on the sequencing data. The dental plaque flora associated with the disease was discovered, and the dental plaque flora was integrated with the disease information to help medical staff to distinguish whether the patient to be tested is a glioma patient or a meningioma patient to the greatest extent.

[0024] Through metagenomic sequencing, the present invention discovered five microorganisms with a high correlation with brain gliomas and meningiomas, including: Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.

[0025] Further analysis and verification showed that the above five microorganisms were significantly associated with brain gliomas and meningiomas. Specifically, the content of Capnocytophaga gingivalis and Selenomonas putigena was higher (even significantly increased) in the brain glioma group, and the content of Prevotellashahii, Leptotrichia buccalis and Ottowia sp. was higher (even significantly increased) in meningiomas.

[0026] ROC curve analysis showed that the above five microbial markers had high specificity and sensitivity as detection variables. Therefore, these five bacterial species can be used as detection markers to diagnose whether the patient is a glioma patient or a meningioma patient.

[0027] In actual work, the staff will detect Ottowia sp. as a certain species of Ottowia, whose NCBI taxonomy number is 1898956 (NCBITaxonomyID: 1898956). The Ottowia sp. with the above NCBI taxonomy number 1898956 is a known species in this field and will not be described in detail here.

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and examples. The following examples are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. Experimental methods not specifying specific conditions in the examples are generally based on conventional conditions. Example 1: Sample collection and extraction 1.1. Collect dental plaque samples from 100 patients with glioma and 100 patients with meningioma: The sample sources and inclusion criteria for the glioma group are as follows: patients were recruited from Zhongnan Hospital of Wuhan University. Inclusion criteria were: 1. Age older than 18 years; 2. Diagnosis of glioma (diagnosed by imaging or confirmed by histopathology or molecular pathology); 3. No antibiotic or immunosuppressive treatment within 1 month before biological sample collection; 4. Patients or their guardians agreed to participate in the study and signed an informed consent form.

[0029] Exclusion criteria for the glioma group: 1. Patients with other malignant tumors; 2. Patients with severe oral diseases or receiving oral treatment within the past month; 3. Patients with severe liver disease and kidney damage or receiving continuous renal replacement therapy, hemodialysis or peritoneal dialysis; 4. Patients who are unable to cooperate in the collection of dental plaque samples.

[0030] The sample sources and inclusion criteria for the meningioma group are as follows: patients were recruited from Zhongnan Hospital of Wuhan University. Inclusion criteria were: 1. Age older than 18 years; 2. Diagnosis of meningioma (radiological or histopathological diagnosis); 3. No antibiotic or immunosuppressant treatment within 1 month before sampling; 4. Patients or their guardians agreed to participate in the study and signed an informed consent form.

[0031] Exclusion criteria for the meningioma group: 1. Patients with other malignant tumors; 2. Patients with severe oral diseases or receiving oral treatment within the past month; 3. Patients with severe systemic diseases and active infections; 4. Patients who are unable to cooperate with the collection of dental plaque samples.

[0032] 1.2 Sample extraction First, rinse the collection area with sterile saline (wash your mouth thoroughly for about 10 seconds, repeat 2 to 3 times), and after wetting the cotton roll, use the collection swab to collect dental plaque from the lingual surface, labial surface, caries lesion area and other parts of the teeth. When collecting, scrape the collection area back and forth 3 to 4 times, then turn the collection swab over and continue scraping the same area 3 to 4 times. Each sample is collected with one collection swab. After the collection is completed, place the collection swab with dental plaque in a sampling tube containing preservation solution (only 1 / 5 of the protective solution remains), stir for about 30 to 40 seconds, shake the sampling tube quickly for 30 seconds, break the collection swab, and seal the sampling tube. At this time, the dental plaque sample can be obtained.

[0033] Example 2: DNA extraction, library construction and sequencing 1. Select the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected dental plaque samples.

[0034] 2. After extraction, the DNA concentration was detected using Qubit and the integrity of the extracted genomic DNA was detected by 1.5% agarose gel electrophoresis. The extracted genomic DNA was quality tested to screen out qualified genomic DNA samples (DNA concentration ≥ 20 ng / μL, volume ≥ 20 μL, total amount ≥ 400 ng).

[0035] 3. For qualified DNA samples, random shearing, end repair, ligation of A bases, addition of adapters and indexes, and purification and library amplification after adapter ligation are performed. After amplification, the DNA concentration is tested (DNA concentration ≥ 40 ng / μL).

[0036] 4. After the libraries pass the test, the different libraries are pooled according to the effective concentration and target data volume before sequencing. The metagenomic sequencing platform used is the BGI T7, and the sequencing strategy is PE150.

[0037] Example 3: LEfSe analysis and screening of dental plaque microbial markers 1. Divide the dataset KneadData software was used to perform quality control (based on Trimmomatic) and host removal (based on Bowtie2) on the raw data. Kraken2 was used to calculate the number of species sequences contained in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% of the subjects to be tested (including those 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. The abundance data of each sample in the training set was then analyzed using LEfSe software, with the default LDA Score screening value set to 2.5. The sample information table is shown in Table 1. Table 1 Sample information table

[0038] The results are as follows Figure 1 As shown, this application discovered for the first time five bacterial species related to brain gliomas. Specifically, the staff screened out two microorganisms with higher (or even significantly increased) content in the meningioma group, including Capnocytophaga gingivalis and Selenomonas sputigena; at the same time, the staff screened out three microorganisms with higher (or even significantly increased) content in the brain glioma group, including Prevotella shahii, Leptotrichia buccalis, and Ottowia sp.

[0039] Example 4: Verification of the reliability of the above five dental plaque microorganisms 1. First, the remaining 20% ​​of the subjects to be tested in Example 1 (including the subjects in the glioma group and the healthy group) were used as the validation set. The abundance data of each sample in the validation set were first subjected to binary logistic regression operation, and then the receiver operating characteristic curve test (ROC curve) analysis was performed to obtain the cutoff value (optimal cutoff value).

[0040] 2. Specificity and sensitivity were calculated and ROC curves were drawn using IBM SPSS Statistics (v27) statistical software. The software first calculated the threshold of the actual measurement value, and then calculated the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold. Specificity (true negative rate) = TN / (TN+FP), Sensitivity (true positive rate) = TP / (TP+FN), 3. The ROC curve can be constructed by dividing specificity and sensitivity by 1. The integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain indicator, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum Youden coefficient are the specificity and sensitivity of the indicator.

[0041] 4. The relative abundance of dental plaque microorganisms of a single strain was directly analyzed by receiver operating characteristic curve test (ROC curve) to obtain the cutoff value (optimal cutoff value). The ROC curve of the prediction score is as follows: Figure 3 The AUC, optimal cutoff value, sensitivity, and specificity of the predicted mimicry markers (markers formed by the combination of five single bacterial species) and individual bacteria are shown in Table 2.

[0042] Table 2 ROC diagnostic curve results

[0043] As shown in Table 2, for single bacterial species, Ottowia sp. had the highest AUC value (0.875), and Capnocytophaga gingivalis had the lowest AUC value (0.655). The AUC value of the mimicry marker (a marker formed by the combination of five single bacterial species) was significantly higher than that of the single bacterial species.

[0044] From the above, it can be seen that the five newly discovered bacterial species in this application can all be used as detection variables, they all have high specificity and sensitivity, and the AUCs of the five dental plaque microbial markers are all greater than 65%. Therefore, one or more of the five dental plaque microbial markers can be used as detection markers for the diagnosis of meningioma patients.

[0045] Example 5: Logistic regression model establishment Based on the above-screened dental plaque microbial markers and the relative abundance values ​​of each metabolite marker, the binary logistic regression algorithm in sp.SS software was used to calculate the first probability of disease for each sample (that is, the logarithm y of the odds of the patient to be tested). On this basis, the disease probability optimization formula Z = exp (y) / {1 + exp (y)} was used to calculate the disease probability Z of the sample to be tested. Finally, the disease probability Z was compared with the actual disease condition (such as severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically: a. Build a model Through the biomarkers mined above, based on the ratio of brain glioma population and patient population in the training set, we further used the relative abundance values ​​of the five detected bacterial species as a single variable. On this basis, we discussed the linear relationship between the relative abundance values ​​of the five single bacteria and the probability of disease in the sample, and calculated the logarithm y of the odds of the patient to be tested (also known as the first probability value y) through the binary logistic regression equation: y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5; Among them, A is the intercept term, B1-B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Prevotellashahii, 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.

[0046] b. Determine the values ​​of A and B1 to B5 above After statistical analysis of the sample data, the values ​​of A and B1 to B5 are: 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; At this point, after sorting, the calculation formula for the logarithm y of the odds is:

[0047] c. Calculate the probability Z of the patient to be tested Substitute the above-mentioned first probability value y into the following formula to calculate the probability Z that the patient to be tested is a patient: Z=exp(y) / {1+exp(y)}; wherein Z is the probability value of the patient to be tested is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0048] d. Validation set data calculation and statistical analysis Based on the data of the validation set, the relative abundance of each single bacterial species in each sample in the disease group and the healthy group was obtained. Then, the aforementioned binary logistic regression method was used to obtain the first probability value y. The probability Z that the sample to be tested was a patient was calculated using the formula. The results are shown in Tables 3 and 4.

[0049] Table 4 shows the relative abundance mean and standard deviation of each bacterial species. The relative abundance mean determines the center position of the data distribution, while the standard deviation reflects the degree of dispersion of the data relative to the mean. The P value is a statistic 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.

[0050] Table 3 Correlation numbers of validation set markers

[0051]

[0052] Note: E is used to represent the power of 10. For example, 3.08043002803e-05 means 3.08043002803×10 -5 .

[0053] Table 4. Statistical data of the relative abundance of markers in the validation set

[0054] Note: In Table 4, the mean refers to the relative abundance mean, and the same applies to the standard deviation.

[0055] e. Results and Analysis Combining the results of Example 4 and Example 5, it can be seen that the AUC of the prediction score of the mimicry marker (combination of 5 bacterial species) is 1, the optimal cutoff value is 0.319170805, the sensitivity is 1, and the specificity is 1. Therefore, the mimicry marker can be used to distinguish whether the patient to be tested is a glioma patient or a meningioma patient. It is completely non-invasive, has a better predictive effect, and has a high accuracy rate.

[0056] Combining the records in Table 3 above and the results obtained by the calculation formula of the probability of disease Z, it can be seen that the calculation formula for calculating the probability of disease of the sample to be tested summarized in this application is basically correct, and can distinguish whether the patient to be tested is a glioma patient or a meningioma patient; there are cases in Table 3 above that do not fully meet the diagnostic criteria. The reason is that the dental plaque samples of the patients to be tested may have false positive results or false negative results, and further testing is required using other means, including blood routine, diagnostic physical signs, etc.

[0057] Further research found that Figure 1 As shown in the data, among the five microorganisms discovered in the present invention, Capnocytophaga gingivalis and Selenomonas sputigena were present in higher levels (even significantly increased) in the meningioma group, and Prevotella shahii, Leptotrichia buccalis and Ottowia sp were present in higher levels (even significantly increased) in the brain glioma group.

[0058] If it is difficult to diagnose a patient and distinguish whether it is meningioma or glioma, medical staff can test the patient's dental plaque and combine it with the above Figures 1 to 3 Based on the information in Tables 2 to 4, the patient is predicted to have either meningioma or glioma using the following method: (1) If the patient is observed for multiple consecutive periods and the levels of Capnocytophaga gingivalis and Selenomonas sputigena in his dental plaque are high (compared with the mean and standard deviation of the glioma group in Table 4), or even show a significant increasing trend, then the patient is more likely to be a meningioma patient.

[0059] (2) If the patient is found to have a high content of one or more of Prevotella shahii, Leptotrichia buccalis, and Ottowia sp in his dental plaque after multiple consecutive observation periods (compared with the mean and standard deviation of the meningioma group in Table 4), then the patient is more likely to be a glioma patient.

[0060] (3) If the patient does not show the pattern in (1) and (2) above after continuous observation for multiple periods of time, the medical staff can combine Figure 1 and Table 4, to make a tendency judgment on which specific disease the patient has; (4) If medical personnel want to more accurately determine whether the patient has meningioma or glioma, they can use the relative abundance values ​​of the five single bacterial species and refer to the technical solutions described in Examples 5 and 6 to calculate whether the patient has meningioma or glioma. (See the description of Examples 6 and 7 for details.)

[0061] It should be noted that the patient to be tested referred to in this embodiment is a patient who is preliminarily diagnosed as a glioma or meningioma by medical staff 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 meningioma, especially when the patient is unwilling to undergo an invasive biopsy (invasive biopsy involves surgical trauma and the risk of neurological damage).

[0062] Example 6: Computer program product related to glioma and meningioma Based on the above embodiments, this embodiment provides a computer program product related to glioma and meningioma. The computer program product is used to execute a method for diagnosing whether a patient to be tested has a risk of glioma, comprising the following steps: S1. Obtain the relative abundance of each bacterial species in the dental plaque of the patient to be tested; the single bacterial species include Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis, and Ottowia sp.; S2. Substituting the relative abundance values ​​of each single bacterial species into the same binary logistic regression equation, and calculating the logarithm y of the odds of the patient to be tested (i.e., the first probability value y); ; Among them, 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 putigena, x4 is the relative abundance value of Leptotrichia buccalis, and x5 is the relative abundance value of Ottowia sp.; S3. Calculate the probability Z of the patient being tested having a meningioma based on y, where Z = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y. After sorting, the probability of illness Z can also be expressed as:

[0063] S4. Determine whether the patient to be tested is a glioma patient or a meningioma patient based on a comparison of the patient's probability Z value with a reference value.

[0064] In practice, when the Z value is greater than 0.5, it indicates a high probability that the patient has a meningioma; when the Z value is less than 0.5, it indicates a high probability that the patient has a glioma; and when the Z value is 0.5, it indicates that the patient may have a meningioma or a glioma. In this case, further testing is necessary using other methods, such as blood tests and physical signs. Furthermore, the closer the Z value is to 0.5, the more necessary it is to use other methods for testing.

[0065] Example 7: Differentiating between patients with glioma and patients with meningioma Based on the product and method of Example 6, this embodiment further provides a method for determining whether a patient to be tested is a glioma patient or a meningioma patient. The specific steps are as follows: S1. Collect dental plaque samples from patients to be tested (see Examples 1-3), and detect the relative abundance of each single bacterial strain in the dental plaque; wherein the single bacterial strains include Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis, and Ottowia sp.; S2. Calculate the logarithm y of the odds of the patient to be tested based on the binary logistic regression equation; ; 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 putigena, x4 is the relative abundance value of Leptotrichia buccalis, and x5 is the relative abundance value of Ottowia sp.; S3. Calculate the probability Z of the patient being tested having a meningioma based on y, where Z = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y. The probability of disease Z can also be expressed as:

[0066] S4. According to the comparison result of the disease probability Z and the reference value, determine whether the patient to be tested is a glioma patient or a meningioma patient.

[0067] In practice, when the Z value is greater than 0.5, it indicates a high probability that the patient has a meningioma; when the Z value is less than 0.5, it indicates a high probability that the patient has a glioma; and when the Z value is 0.5, it indicates that the patient may have a meningioma or a glioma. In this case, further testing is necessary using other methods, such as blood tests and physical signs. Furthermore, the closer the Z value is to 0.5, the more necessary it is to use other methods for testing.

[0068] It should be noted that the patient to be tested referred to in this embodiment is a patient who is preliminarily diagnosed as a glioma or meningioma by medical staff 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 meningioma, especially when the patient is unwilling to undergo an invasive biopsy (invasive biopsy involves surgical trauma and the risk of neurological damage).

[0069] Example 8: Detection Reagents Based on the description of the above embodiments 1 to 5, it can be seen that the markers selected in this application have a good predictive effect. Medical staff can use a single bacterial species as a marker to detect and diagnose the sample to be tested alone to determine whether the patient to be tested is a glioma patient or a meningioma patient; medical staff can also combine multiple single bacterial species as markers to detect and diagnose the sample to be tested to more accurately determine whether the patient to be tested is a glioma patient or a meningioma patient.

[0070] Therefore, this embodiment also provides a reagent for detecting dental plaque microbial markers, which can be used in the preparation of a product for distinguishing between glioma and meningioma to diagnose whether the patient to be tested is a glioma patient or a meningioma patient.

[0071] At the same time, the dental plaque microbial markers described in the present invention can be selected from the five single bacterial species discovered in this application and associated with brain glioma and meningioma, that is, the dental plaque microbial markers in the detection reagent can be selected from one or more of Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.

[0072] Example 9: Kit This embodiment also provides a kit that can include the detection reagent described in Example 7 to determine whether the patient being tested is a glioma patient or a meningioma patient. The limitations and technical solutions of this application regarding the kit can be found in the description of Example 7 above and are not described in detail here. Similarly, the kit can also be used in the preparation of a product for distinguishing between gliomas and meningiomas, which is not described in detail here.

[0073] Example 10: Product for differentiating between patients with glioma and patients with meningioma The present application also provides a product for diagnosing whether a patient to be tested is a glioma patient or a meningioma patient, so as to determine whether the patient to be tested is a glioma patient or a meningioma patient; the product is specific to one or more of the five single bacterial species discovered in the present application and related to gliomas and meningiomas, and the product includes primers, probes, antibodies, aptamers or chips.

[0074] It can be seen from the description of Examples 1 to 5 and the conventional means in the field that when the newly discovered five single bacterial species are used as dental plaque microbial markers, it should be feasible for those skilled in the art to produce corresponding specific products (primers, probes, antibodies, aptamers or chips, etc.), and no further details will be given here.

[0075] Conclusion and explanation: 1. By Figures 1 to 3As shown in Table 2, any one of the five newly discovered single bacterial species in this application can be used as a microbial marker to distinguish patients with glioma from patients with meningioma. Each single bacterial species has sensitivity and specificity for both glioma and meningioma. Therefore, the dental plaque microbial marker can be selected from any one or more of the five newly discovered single bacterial species in this application. Specifically: 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.

[0076] 2. As can be seen from Table 3, when testing dental plaque samples, only one or several bacterial species are detected. This is a normal phenomenon. This is because individuals have differences. The disease probability Z of this application is obtained through calculation. Therefore, even if a sample only contains a single bacterial species, this application can calculate the probability that the sample to be tested (the patient to be tested) has meningioma, thereby helping medical staff to distinguish between patients with glioma and patients with meningioma.

[0077] 3. Prediction effect: For single bacterial species, Ottowia sp. had the highest AUC value (0.875), and Capnocytophaga gingivalis; the AUC value of the mimicry marker (a marker formed by the combination of 5 single bacterial species) was significantly higher than the AUC value of a single bacteria.

[0078] At the same time, the five newly discovered bacterial species in this application can all be used as detection variables, all of which have high specificity and sensitivity, and the AUCs of the five dental plaque microbial markers are all greater than 65%. One or more of the five dental plaque microbial markers can be used as a basis for determining whether the patient to be tested has brain glioma or meningioma.

[0079] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent replacements and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention should be included in the scope of protection of the invention.

Claims

1. A dental plaque microbial marker associated with glioma and meningioma, characterized in that: The dental plaque microbial markers include Prevotella shahii and Selenomonas putigena.

2. The dental plaque microbial marker according to claim 1, characterized in that The dental plaque microbial markers further include one or more of Ottowia sp., Capnocytophaga gingivalis and Leptotrichia buccalis.

3. The dental plaque microbial marker according to claim 2, characterized in that The dental plaque microbial marker is a marker combination consisting of Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis and Ottowia sp.

4. Use of a reagent for detecting the dental plaque microbial marker according to any one of claims 1 to 3 in the preparation of a product for distinguishing between glioma and meningioma.

5. A kit, characterized in that: The kit contains a reagent for detecting the dental plaque microbial marker according to any one of claims 1 to 3.

6. Use of the kit according to claim 5 in preparing a product for distinguishing between glioma and meningioma.

7. A product for diagnosing brain glioma, characterized by: The product comprises primers, probes, antibodies, aptamers or chips that are specific to the dental plaque microbial markers according to any one of claims 1 to 3.

8. A computer program product related to glioma and meningioma, characterized in that: The computer program product is used to execute a method for diagnosing whether a patient to be tested has a risk of having a brain glioma, the method comprising: Obtain the relative abundance value of each single bacterial species in the dental plaque of the patient to be tested; Substituting the relative abundance values ​​of each single bacterial species into the same binary logistic regression equation simultaneously to calculate the logarithm y of the odds of the patient to be tested, the single bacterial species including Prevotella shahii, Capnocytophaga gingivalis, Selenomonas sputigena, Leptotrichia buccalis, and Ottowia sp.; Calculate the probability Z of the patient being diagnosed with meningioma based on y, Z=exp(y) / {1+exp(y)}, where exp(y) is the exponential function of y; According to the comparison result of the disease 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, wherein: The formula of the binary logistic regression equation is: y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5 Among them, A is the intercept term, B1-B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Prevotellashahii, 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.

10. The product according to claim 9, characterized in that: 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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