Brain glioma intestinal microbial marker, product and application thereof

Abstract

The invention discloses a brain glioma intestinal microbial marker, a product and application of the brain glioma intestinal microbial marker, and belongs to the field of biological medicine. The invention relates to an enteric microbial marker, which is characterized by being prepared from one or more of Eubacter algenti, Prevotella copri, Alistipes finegili, Bifidobacterium adolescentis and Citrobacter sp., and the enteric microbial marker is prepared from one or more of Eubacter algenti, Prevotella copri, Alistipes finegili, Bifidobacterium adolescentis and Citrobacter sp., and the enteric microbial marker is prepared from one or more of Eubacter algenti, Prevotella copri, Alistipes finegili, Bifidobacterium adolescentis and Citrobacter sp. The invention provides a kit which comprises a detection reagent for detecting the relative abundance of the intestinal microbial marker. The invention provides a product for diagnosing brain glioma and a computer program product related to the brain glioma. The product provided by the invention has good feasibility and accuracy, and provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and particularly relates to intestinal microbial markers for glioma, products and their applications. Background Art

[0002] In current clinical practice, the methods for diagnosing glioma are mainly divided into the following categories: computed tomography (CT), magnetic resonance imaging (MRI), and molecular typing diagnosis.

[0003] Computed tomography is a preliminary screening method for glioma, which can quickly detect intracranial space-occupying lesions. However, it has problems such as low resolution for soft tissues and easy missed diagnosis (misdiagnosis); magnetic resonance imaging (MRI) is the core imaging tool for glioma diagnosis, which can provide information on the anatomy, function, and molecular characteristics of tumors and can be used to assist preoperative planning and prognosis evaluation. However, it has the problems of high examination costs and the need to be used in combination with molecular typing diagnosis; molecular typing is the "gold standard" for glioma diagnosis, but it requires obtaining pathological tissues through surgery (or biopsy), thus having the problems of trauma risk and possible delay in diagnostic decisions.

[0004] In summary, the existing diagnostic models for glioma urgently need to be broken through in terms of accuracy, safety, and personalization. Currently, there is still a lack of a clear, safe, and non-invasive glioma diagnostic product, thus providing a new idea and approach for the diagnosis of glioma. Summary of the Invention

[0005] In view of the above technical problems, the present invention provides an intestinal microbial marker for glioma, a product and its application, which are clear, safe, and non-invasive, and can provide a new idea and approach for the diagnosis of glioma.

[0006] The technical solutions provided by the present invention are as follows: In the first aspect, an intestinal microbial marker related to glioma is provided, and the intestinal microbial marker includes Eubacterium eligens and Prevotella copri.

[0007] In the above technical solution, the intestinal microbial marker further includes one or more of Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.

[0008] In the above technical solution, the intestinal microbial markers include Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.

[0009] In a second aspect, provided is an application of a reagent for detecting the intestinal microbial markers described in the first aspect in the preparation of a product for diagnosing glioblastoma.

[0010] In a third aspect, a kit is provided, and the kit contains a reagent for detecting the intestinal microbial markers described in the first aspect.

[0011] In a fourth aspect, provided is an application of the kit described in the third aspect in the preparation of a product for detecting glioblastoma.

[0012] In a fifth aspect, provided is a product for diagnosing glioblastoma, and the product includes primers, probes, antibodies, aptamers, or chips that are specific to the intestinal microbial markers described in the first aspect.

[0013] Specifically, provided is a product for diagnosing glioblastoma, and the product is used to detect the relative abundance value of each single strain in the intestinal microbial markers described in the first aspect. The product includes primers, probes, antibodies, aptamers, or chips that are specific to the intestinal microbial markers.

[0014] In a sixth aspect, provided is a computer program product related to glioblastoma, and the computer program product is used to execute a method for diagnosing the risk of whether a subject to be tested has glioblastoma. The method includes: Obtaining the relative abundance value of each single strain in the fecal sample of the subject to be tested; Substituting the relative abundance value of each single strain into a binary logistic regression equation to calculate the logarithm of the odds y of the subject to be tested. The single strains include Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.; Calculating the probability Z of the subject to be tested being a glioblastoma patient according to y, , where is an exponential function of y; Diagnose or predict the risk of the object to be tested suffering from glioma according to the comparison result of the disease probability Z and the reference value.

[0015] In the above technical solution, the formula of the binary logistic regression equation is:

[0016] Where A is the intercept term, and B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri in feces, x3 is the relative abundance value of Alistipes finegoldii, x4 is the relative abundance value of Eubacterium eligens, and x5 is the relative abundance value of Citrobacter sp.

[0017]

[0015] In the above technical solution, the formula of the binary logistic regression equation is:

[0016] Where A is the intercept term, and B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri in feces, x3 is the relative abundance value of Alistipes finegoldii, x4 is the relative abundance value of Eubacterium eligens, and x5 is the relative abundance value of Citrobacter sp.

[0017] In the above technical solution, A is 2.87, B1 is -315.6, B2 is -5.983, B3 is 3746, B4 is 597.2, and B5 is -2550.

[0018] The beneficial effects of the present invention are as follows: 1. The present invention newly discovers 5 kinds of gut microbes related to glioma, including Eubacterium eligens, Prevotella copri in feces, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.; after research and verification, it is found that one or several of the above 5 kinds of gut microbes can be used as gut microbial markers for glioma to diagnose whether the sample to be tested suffers from glioma.

[0019] 2. The present invention also provides a reagent and a kit, which can use one or several of the above 5 strains as detection markers to predict or diagnose glioma, completely non-invasive and with high accuracy. Through metagenomic sequencing, higher resolution is provided, enabling the analysis of microbial communities to reach the species or even strain level, thereby improving the accuracy and reliability of diagnosis. The above 5 strains can also be used as target microbes for developing these systems, filling the gap in this field.

[0020] 3. The present invention also provides a product for diagnosing glioma. This product can calculate the probability of disease based on the relative abundances of various bacterial species, and then compare it with a reference value to predict or diagnose whether a patient has glioma or is at risk of having glioma. This product has good feasibility and accuracy, can effectively evaluate the risk of glioma, and provides a new tool for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a result graph of linear discriminant analysis; Figure 2 It is a box scatter plot of intestinal microbial markers; Figure 3 It is an ROC curve. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to evaluate whether the composition of intestinal symbiotic flora can be used as a predictor of glioma, the present invention collected samples from glioma patients and healthy people, performed metagenomic sequencing and used bioinformatics to statistically analyze the sequencing data, discovered intestinal flora related to the disease, integrated the intestinal flora with disease information, and predicted glioma patients to the greatest extent.

[0023] Through metagenomic sequencing, the present invention discovered the correlations of Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp. with glioma patients.

[0024] After verification, the significant associations between the above 5 species of bacteria and glioma are as follows: The staff screened out 1 marker that was significantly increased in the glioma group, including Alistipes finegoldii; 4 markers that were significantly decreased in the glioma group, including Bifidobacterium adolescentis, Citrobacter sp., Eubacterium eligens, and Prevotella copri.

[0025] Through ROC curve analysis, the above 5 markers have high specificity and sensitivity as detection variables. Therefore, these 5 species of bacteria can be used as detection markers for the prediction and diagnosis of glioma patients.

[0026] During actual work, the Citrobacter sp. detected by the staff is a species of Citrobacter, and its NCBI taxonomy number is 1898956 (NCBI Taxonomy ID: 1898956). The Citrobacter sp. with the above NCBI taxonomy number 1898956 is a known species in the field and will not be described in detail here.

[0027] 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 Fecal samples were collected from 65 patients with brain glioma and 75 healthy subjects. 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.

[0028] Exclusion criteria for the glioma group: 1. Patients with other malignant tumors; 2. Patients with severe liver disease and kidney damage or receiving continuous renal replacement therapy, hemodialysis, or peritoneal dialysis; 3. Patients unable to cooperate with the collection of stool samples.

[0029] Inclusion criteria for the control group: 1. Age distribution is over 18 years old; 2. No history of neurological disease and no abnormalities in physical examination in the past year; 3. Good eating habits and lifestyle.

[0030] Exclusion criteria for the control group: 1. Suffering from severe infection or chronic gastrointestinal inflammatory disease in the past three months; 2. Unable to cooperate with the collection of stool samples.

[0031] 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 stool samples.

[0032] 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).

[0033] 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).

[0034] 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.

[0035] Example 3: LEfSe analysis and screening of intestinal 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

[0036] The results are as follows Figure 1 As shown, this application discovered for the first time 5 bacterial species related to brain glioma. Specifically: the staff screened out 1 marker that was significantly increased in the brain glioma group, including Alistipes finegoldii; 4 markers that were significantly reduced in the brain glioma group, including Bifidobacterium adolescentis, Citrobacter sp., Eubacterium eligens, and Prevotella copri.

[0037] Example 4: Verification of the reliability of the above five intestinal microbial markers 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).

[0038] 2. Use IBM SPSS Statistics (v27) statistical software to complete the calculation of specificity and sensitivity and the drawing of the ROC curve. First, the software calculates the threshold of the actual measured value internally, and then calculates the 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 through 1 - specificity and sensitivity, and the integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of an index, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of a certain index.

[0039] 4. The relative abundance values of the intestinal microbial markers of a single strain are directly subjected to the receiver operating characteristic curve test (ROC curve) analysis to obtain the cutoff value (optimal cut-off value). The ROC curve of the prediction score is as Figure 3 shown. The AUC, optimal cut-off value, sensitivity, and specificity of the mimic marker (the marker formed by the combination of 5 single strains) and single bacteria are shown in Table 2.

[0040] Table 2 Results of the ROC diagnostic curve

[0041] According to Table 2, for a single strain, the AUC value of Eubacterium eligens is the highest (0.8564), and the AUC value of Alistipes finegoldii is the lowest (0.6538); the AUC value of the mimic marker (the marker formed by the combination of 5 single strains) (0.9538) is significantly higher than that of single bacteria.

[0042] As can be seen from the above: The 5 strains newly discovered in this application can all be used as detection variables, and they all have high specificity and sensitivity, and the AUC of the 5 intestinal microbial markers is greater than 65%. Therefore, one or more of the 5 intestinal microbial markers can be used as detection markers for the diagnosis of glioma patients.

[0043] Example 5: Establishment of a logistic regression model Based on the above-selected intestinal microbial markers and obtaining the relative abundance values of each metabolic marker, use the binary logistic regression algorithm in SPSS software to calculate the first disease probability of each sample (that is, the logarithm of the odds y of the object to be tested), and on this basis, use the disease probability optimization formula Calculate the disease probability Z of the sample to be tested, and finally compare this disease probability Z with the actual disease situation (such as severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically: a. Establish a model Based on the biomarkers mined above, and based on the proportions of glioma patients and non-patients in the training set, further use the relative abundance values of the 5 detected bacterial species as single variables, and on this basis, discuss the linear relationship between the relative abundance values of the 5 single bacteria and the disease probability of the sample. Calculate the logarithm of the odds y (also known as the first probability value y) of the object to be tested through a binary logistic regression equation: ; where A is the intercept term, and B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri in feces, x3 is the relative abundance value of Alistipes finegoldii, x4 is the relative abundance value of Eubacterium eligens, and x5 is the relative abundance value of Citrobacter sp.

[0044] b. Determine the values of A and B1 to B5 above After performing statistical analysis on the sample data, the values of A and B1 to B5 above are: A is 2.87, B1 is -315.6, B2 is -5.983, B3 is 3746, B4 is 597.2, B5 is -2550: After sorting ; c. Calculate the disease probability Z of the object to be tested Substitute the first probability value y above into the following formula to calculate the probability Z that the object to be tested is a patient: ; where Z is the probability value that the object to be tested is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0045] d. Calculation and statistical analysis of validation set data Based on the data of the validation set, obtain the relative abundance of each single bacterial species in the disease group and the healthy group of each sample, then use the aforementioned binary logistic regression method to obtain the first probability value y, and then calculate the probability Z that the sample to be tested is a patient through the formula. The results are shown in Tables 3 and 4.

[0046] 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.

[0047] Table 3 Data related to the validation set markers

[0048] Note: E is used to represent the power of 10. For example, 9.64739E-05 means 9.64739×10 -5 Table 4. Statistical data of the relative abundance of markers in the validation set

[0049] Note: In Table 3, the mean refers to the relative abundance mean, and the same applies to the standard deviation.

[0050] e. Results and Analysis Combining the results of Examples 4 and 5, it can be seen that the AUC for the predictive score of the mimicry marker is approximately 95.38%, the optimal cutoff value is approximately 0.2937, the sensitivity is approximately 0.9231, and the specificity is approximately 0.8667. Therefore, the application of mimicry markers as detection markers in the diagnosis of patients with brain glioma has better results and higher accuracy. Using these five bacterial species as detection markers is completely non-invasive and highly accurate.

[0051] Combining the records in Table 3 above and the results obtained by the calculation formula of the disease probability Z, it can be seen that the calculation formula for calculating the disease probability of the sample to be tested summarized in this application is basically correct and can be used to diagnose the disease risk and disease probability of the sample to be tested; the above Table 3 has situations that do not fully meet the diagnostic criteria. The reason is that the stool samples of the persons to be tested may have false positive results or false negative results, and further testing is required by other means, including blood routine, diagnostic physical signs, etc.

[0052] Example 6: Computer program product related to brain glioma Based on the above embodiments, this embodiment provides a computer program product related to brain glioma, wherein the computer program product is used to execute a method for diagnosing whether a subject has a risk of having brain glioma, comprising the following steps: S1. Obtain the relative abundance values of each single bacterial species in the fecal sample of the subject to be tested; the single bacterial species include Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.; S2. Substitute the relative abundance values of each single bacterial species into the binary logistic regression equation to calculate the logarithm of the odds y (i.e., the first probability value y) of the subject to be tested; ; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri, x3 is the relative abundance value of Alistipes finegoldii, x4 is the relative abundance value of Eubacterium eligens, and x5 is the relative abundance value of Citrobacter sp.; S3. Calculate the disease probability Z of the subject to be tested as a glioma patient based on y, ; exp(y) is the natural exponential function of y; The disease probability Z can also be expressed as:

[0053] S4. Based on the comparison of the probability Z value of the patient with the reference value, diagnose or predict the risk of the subject to be tested having glioma.

[0054] In actual work, when the Z value is greater than 0.5, it means that the probability of the subject to be tested having glioma is small; when the Z value is less than 0.5, it means that the probability of the subject to be tested having glioma is large; when the Z value is 0.5, it means that the person to be tested may be a patient or a glioma patient. At this time, other means need to be further used for detection, and the other means are blood routine and physical signs diagnosis. Further, the closer the Z value is to 0.5, the more other means are needed for detection.

[0055] Example 7: Check the disease probabilities of patients in the validation set and glioma patients Based on the product and method of Example 5, check the disease probabilities of healthy people and glioma patients in the validation set. The specific steps are as follows: S1. Collect the fecal samples of the person to be tested, and detect the relative abundance value of each single strain in the fecal samples; wherein, the single strains include Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp. S2. Calculate the logarithm of the odds y of the object to be tested according to the binary logistic regression equation. ; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri, x3 is the relative abundance value of Alistipes finegoldii, x4 is the relative abundance value of Eubacterium eligens, and x5 is the relative abundance value of Citrobacter sp. S3. Calculate the probability of disease Z that the object to be tested is a glioma patient according to y. ; exp(y) is the natural exponential function of y. The probability of disease Z can also be expressed as:

[0056] S4. According to the comparison between the probability P value of the patient and the reference value, diagnose or predict the risk that the object to be tested has glioma.

[0057] In actual work, when the P value is greater than 0.5, it means that the probability that the object to be tested has glioma is small; when the P value is less than 0.5, it means that the probability that the object to be tested has glioma is large; when the P value is 0.5, it means that the object to be tested may be a patient or a glioma patient. At this time, other means need to be used for further detection, and the other means are blood routine and physical signs diagnosis. Further, the closer the P value is to 0.5, the more other means are needed for detection.

[0058] Example 8: Detection reagent Based on the descriptions of the above Examples 1-5, it can be seen that the selected markers of the present application have good prediction effects. Medical staff can use a single strain as a marker to separately detect and diagnose the test sample to determine whether the test sample has glioma; medical staff can also combine multiple single strains as markers to detect and diagnose the test sample to determine whether the test sample has glioma.

[0059] Therefore, this embodiment also provides a reagent for detecting intestinal microbial markers, which can be used in the preparation of products for diagnosing glioblastoma to diagnose whether a test sample has glioblastoma; at the same time, the intestinal microbial markers can be selected from 5 single strains found in this application and related to glioblastoma, that is, the intestinal microbial markers in the detection reagent can be selected from one or more of Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.

[0060] Example 9: Kit This embodiment also provides a kit, which can include the detection reagent described in Example 7 to diagnose whether a test sample has glioblastoma; the definition and technical solution of the kit in this application can refer to the description of Example 7 above and will not be elaborated here. Similarly, the above kit can also be used in the preparation of products for detecting glioblastoma and will not be elaborated here.

[0061] Example 10: Product for diagnosing glioblastoma This application also provides a product for diagnosing glioblastoma to diagnose whether a test sample has glioblastoma; the product is specific to one or more of the 5 single strains found in this application and related to glioblastoma, and the product includes primers, probes, antibodies, aptamers or chips.

[0062] Combined with the descriptions of Examples 1-5 and the conventional means in the art, it can be known that when the newly discovered 5 single strains are used as intestinal microbial markers, it should be achievable for those skilled in the art to produce corresponding specific products (such as primers, probes, antibodies, aptamers or chips, etc.), which will not be elaborated here.

[0063] Conclusions and explanations: 1. From Figures 1 to 3 and Table 2, it can be seen that any one of the 5 newly discovered single strains in this application can be used as an intestinal microbial marker for glioblastoma, and each single strain has sensitivity and specificity for glioblastoma. Therefore, the intestinal microbial markers related to glioblastoma can be selected from any one or several of the 5 newly discovered single strains in this application. Specifically: The intestinal microbial markers related to glioma can be selected from one or more of Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.

[0064] 2. As can be seen from Table 3, when detecting fecal samples, it is normal to only detect a certain strain or several strains. This is because individuals have differences. The disease probability Z of this application is calculated. Therefore, even if a single sample only contains a single strain, the probability of the test sample having glioma can still be calculated in this application.

[0065] 3. Prediction effect: For a single strain, the AUC value of Eubacterium eligens is the highest (0.8564), and the AUC value of Alistipes finegoldii is the lowest (0.6538); the AUC value of the mimic marker (a marker formed by combining 5 single strains) (0.9538) is significantly higher than that of single strains. At the same time, the 5 newly discovered strains in this application can all be used as detection variables, and they all have high specificity and sensitivity. Moreover, the AUC of the 5 intestinal microbial markers is greater than 65%. One or more of the 5 intestinal microbial markers can be used as detection markers for the diagnosis of glioma patients.

[0066] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope disclosed by the present invention shall be included in the protection scope of the invention.

Claims

1. An intestinal microbial biomarker related to glioma, characterized in that, The intestinal microbial markers include Eubacterium eligens and Prevotella copri.

2. The intestinal microbial marker according to claim 1, wherein The intestinal microbial markers further include one or more of Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.

3. The intestinal microbial marker according to claim 2, wherein The intestinal microbial markers include Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.

4. Use of a reagent for detecting the intestinal microbial markers according to any one of claims 1 to 3 in the preparation of a product for diagnosing glioblastoma.

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

6. Use of a kit according to claim 5 in the preparation of a product for detecting glioblastoma.

7. A product for diagnosing glioma, characterized in that: The product includes primers, probes, antibodies, aptamers, or chips specific for the intestinal microbial markers according to any one of claims 1 to 3.

8. A computer program product related to glioma, characterized in that: The computer program product is used to execute a method for diagnosing the risk of a subject to be tested having glioblastoma, and the method includes: Obtaining the relative abundance value of each single bacterial species in the fecal sample of the subject to be tested; Substituting the relative abundance value of each single bacterial species into a binary logistic regression equation to calculate the logarithm of the odds y of the subject to be tested, and the single bacterial species includes Eubacterium eligens, Prevotella copri, Alistipes finegoldii, Bifidobacterium adolescentis, and Citrobacter sp.; According to calculate the probability of suffering from glioma for the object to be measured , , where is an exponential function of According to the probability of disease Compare the result with the reference value to diagnose or predict the risk of glioma in the object to be tested.

9. The computer program product according to claim 8, characterized in that: The formula of the binary logistic regression equation is: where A is the intercept term, to are the regression coefficients of the independent variables; is the relative abundance value of Bifidobacterium adolescentis, is the relative abundance value of Prevotella copri in feces, is the relative abundance value of Alistipes finegoldii, is the relative abundance value of Eubacterium eligens, is the relative abundance value of Citrobacter sp.

10. The product according to claim 9, wherein: A is 2.87, is -315.6, is -5.983, is 3746, is 597.2, is -2550.

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