Colorectal polyp intestinal microbe markers and uses thereof

Abstract

The application discloses a colorectal polyp intestinal microorganism marker and application thereof, and belongs to the field of biological medicine. The microorganism marker comprises one or more of the following: Prevotella shahii, Clostridium bolteae, Clostridium ramosum and Veillonella dispar. The application provides a product (such as a kit) comprising detection reagents for detecting the relative abundance of the microorganism marker. The product provided by the application has good feasibility and accuracy, and can effectively evaluate whether a teenager has a risk of colorectal polyp, thereby providing a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to a gut microbial marker for colorectal polyps and its application. Background Technology

[0002] Colorectal polyps are raised lesions protruding into the intestinal lumen from the surface of the colorectal mucosa, and are one of the most common diseases in gastroenterology. Colorectal polyps can be classified into non-neoplastic polyps (hyperplastic polyps, inflammatory polyps, usually harmless) and neoplastic polyps (adenomatous polyps, serrated polyps) according to their pathological type. Among these, adenomatous polyps are the most common pre-polypoid lesions of colorectal polyps, accounting for more than 70% of all polyps.

[0003] Currently, common methods for detecting colorectal polyps include colonoscopy and fecal occult blood test (FIT). Although colonoscopy is the gold standard for diagnosis, it is invasive, requires bowel preparation, and is expensive. Moreover, my country's endoscopic resources are limited, making it difficult to cover the screening needs of a large asymptomatic population. While the fecal occult blood test (FIT) is simple and non-invasive, its sensitivity for advanced adenomas is only about 25% to 40%, resulting in a high rate of missed diagnoses.

[0004] Therefore, there is an urgent need to develop a new, non-invasive, convenient, and highly sensitive detection method. Intestinal microbiota detection based on fecal samples can reflect early changes in the gut microbiota and shows promise in the early warning of colorectal polyps, potentially compensating for the shortcomings of existing screening methods. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a gut microbiota marker for colorectal polyps and its application, which can offer a new approach and method for diagnosing colorectal polyps.

[0006] The technical solution provided by this invention is as follows: In a first aspect, a reagent for quantitatively detecting microbial markers is provided for use in the preparation of products related to colorectal polyps, said microbial markers including Clostridium botulinum bolteae and Alloprevotella rava, which are significantly increased in patients with colorectal polyps.

[0007] In a second aspect, a gut microbial biomarker for diagnosing or predicting colorectal polyps is provided, the microbial biomarker comprising Clostridium bolteae, Alloprevotella rava, Clostridium ramosum, and Veillonella dispar.

[0008] Thirdly, a gut microbiota marker for diagnosing or predicting colorectal polyps is provided, said gut microbiota marker being any of the following combinations: a) The microbial markers are a combination of markers consisting of Clostridium ramosum, Clostridium botleii, and Alloprevotella rava; b) The microbial markers are a combination of markers consisting of Alloprevotella rava, Clostridium bolteae, Clostridium ramosum, and Veillonella dispar.

[0009] Fourthly, a product for diagnosing or predicting colorectal polyps is provided, the product comprising one or more of reagents, test strips, aptamers, and chips, wherein the microbial markers in the product are specific and used for quantitative detection of the microbial markers.

[0010] Fifthly, a kit for diagnosing or predicting colorectal polyps is provided, the kit containing reagents for quantitative detection of the microbial markers.

[0011] Sixthly, the kit described in the fourth aspect is provided for use in the preparation of products for diagnosing colorectal polyps, wherein Clostridium botulinum, Clostridium ramosum, and Alloprevotella rava show a significant increase in the colorectal polyp group, and Veillonella dispar shows a significant decrease in the colorectal polyp group.

[0012] It should be noted that this invention newly discovered and verified a strong correlation between the aforementioned gut microbiota and colorectal polyps, which can be used to diagnose whether a person under testing has colorectal polyps. Based on this, although the embodiments of this invention only list how to achieve quantitative detection of the sample through relative abundance values, other methods for quantitative detection of microorganisms are also feasible (such as absolute abundance or total microbial load information), and can also be used to assist in the diagnosis of whether a person under testing has colorectal polyps. Users can choose according to their own needs, and these will not be elaborated upon here.

[0013] The beneficial effects of this invention are as follows: 1. This invention newly discovers four intestinal microorganisms associated with colorectal polyps, including: Clostridium bolteae, Alloprevotella rava, Clostridium ramosum, and Veillonella dispar.

[0014] After research and verification, it was found that for patients with colorectal polyps, one or more of the above four types of intestinal microorganisms can be used to diagnose whether the person being tested has colorectal polyps.

[0015] 2. The present invention also provides a reagent and / or kit that can use one or more of the above four bacterial species as detection markers (i.e., microbial markers) to diagnose whether the person being tested has colorectal polyps. It is completely non-invasive and highly accurate.

[0016] Metagenomic sequencing provides higher resolution, enabling the analysis of microbial communities to reach the species and even strain level, thereby improving the accuracy and reliability of diagnosis. The four species mentioned can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0017] 3. This invention also provides a product for diagnosing colorectal polyps. This product can diagnose the risk of a person having colorectal polyps based on the relative abundance values ​​of various bacterial species. The entire diagnostic process is safe, non-invasive, and efficient, demonstrating good feasibility and accuracy. It can effectively assess the risk of a person (or their stool sample) having colorectal polyps, and the assessment results are relatively accurate. Attached Figure Description

[0018] Figure 1 The graph shows the results of the linear discriminant analysis on the training set. Figure 2 To validate the box plots of relative abundance of colorectal polyps in patients and healthy individuals; Figure 3 The ROC curve for the prediction score of the validation set. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can understand it.

[0020] This invention provides a microbial biomarker associated with colorectal polyps and its application, resulting in more accurate and representative detection results. It can be used as a predictor of colorectal polyps (including T1DM and T2DM) and can be used to diagnose whether a person under testing has colorectal polyps. Specifically, the technical concept of this invention is as follows: To address the clinical need for diagnosis and detection of colorectal polyps, this invention collects samples from patients with colorectal polyps and healthy individuals, and through a standardized experimental testing procedure (specific experimental methods are described in Examples 1-3), screens out four microorganisms associated with colorectal polyps, including: *Alloprevotella rava*, *Clostridium botleii*, *Clostridium ramosum*, and *Veillonella dispar*.

[0021] ROC curve analysis showed that the above four biomarkers have high specificity and sensitivity as detection variables. These four bacterial species can be used as detection biomarkers for the prediction and diagnosis of colorectal polyps.

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions.

[0023] Example 1: Sample Collection

[0024] The inclusion criteria for fecal samples from patients with colorectal polyps are as follows: 1. Over 50 years of age; 2. Clinically diagnosed with colorectal polyps; 3. Stable vital signs.

[0025] The exclusion criteria for stool samples in the colorectal polyp group were as follows: 1. Received colorectal polyp treatment within 1 month; 2. Taken antibiotics, probiotics, or prebiotics within the past 3 months; 3. Have any chronic disease, including neurobehavioral disorders; 4. Received medications affecting gastrointestinal motility within 1 week; 5. Have a history of functional dyspepsia, aerophagia, or abdominal migraine pain; 6. Showed growth retardation; 7. Have gastrointestinal obstruction or stricture; 8. Have a history of abdominal surgery, peptic ulcer, or inflammatory bowel disease; 9. Patients deemed unsuitable for inclusion in this study by the researchers.

[0026] The inclusion criteria for stool samples from the control group (healthy individuals) were as follows: 1. Over 50 years of age; 2. No depression or other neurological disorders; 3. No diabetes or other metabolic diseases; 4. No colorectal polyps or gastrointestinal diseases; 5. No other immune system diseases or not in an immunodeficient state; 6. No use of antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics before and during the study.

[0027] The same exclusion criteria for fecal samples from the control group (healthy individuals) were used for colorectal polyps.

[0028] The fecal samples used in this invention were collected from Hubei Province. For both colorectal polyp patients and healthy individuals, written informed consent was obtained from both themselves and their legal guardians (if any). Based on the aforementioned inclusion and exclusion criteria, a total of 162 fecal samples were collected from colorectal polyp patients and 124 from healthy individuals. The statistical results are as follows: Table 1 Sample Information Table .

[0029] Example 2: DNA extraction, library construction, and sequencing

[0030] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0031] 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 then subjected to quality control, and qualified genomic DNA samples were selected (DNA concentration ≥20ng / μL, volume ≥20 μL, total amount ≥400 ng).

[0032] 3. For qualified DNA samples, random fragmentation, end repair, A base ligation, adapter and index are added. After adapter ligation, purification and library amplification are performed. After amplification, the DNA concentration is detected (DNA concentration ≥40 ng / μL).

[0033] 4. After the libraries pass the testing, different libraries are pooled according to the effective concentration and target data volume requirements before sequencing. The metagenomic sequencing platform is BGI T7, and the sequencing strategy is PE150.

[0034] Example 3: LEfSe analysis for screening microbial biomarkers

[0035] KneadData software was used for quality control (based on Trimmomatic) and host removal (based on Bowtie2) of the raw data. Kraken2 alignment was used to calculate the sequence number of each species in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% of the participants (including those in the disease and healthy groups) 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 were then analyzed using LEfSe software, with the default LDA Score filter value set to 2. The sample information is shown in Table 1. The results are as follows Figure 1 As shown, the researchers screened out four biomarkers that were significantly increased in the colorectal polyp group, including Alloprevotella rava, Clostridium bolteae, Clostridium ramosum, and Veillonella dispar.

[0036] Example 4: Verifying the reliability of the above four microbial biomarkers

[0037] 1. First, the remaining 20% ​​of the participants in Example 1 (including those in the colorectal polyp 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, and then the receiver operating characteristic (ROC curve) was analyzed to obtain the cutoff value (optimal cutoff value).

[0038] 2. Use R language statistical software to calculate specificity and sensitivity and plot ROC curves. The software first calculates the threshold of the actual measurement value, and then calculates 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).

[0039] 3. The ROC curve can be constructed using 1 minus specificity and sensitivity. 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 value of the Youden coefficient are the specificity and sensitivity of the certain indicator.

[0040] 4. The relative abundance values ​​of microbial biomarkers for single strains were directly analyzed using receiver operating characteristic (ROC) curve testing to determine the cutoff value. The ROC curve for predictive scoring is shown below. Figure 3 As shown in Table 2, the AUC, optimal cutoff value, sensitivity, and specificity of the predicted mimicry markers (markers formed by the combination of four single bacterial species) and individual bacteria are shown in Table 2.

[0041] Table 2. Relevant data on validation set markers .

[0042] As shown in Table 2, for single bacterial species, Clostridium multiflorum has the highest AUC value (approximately 0.871), while Veillonella spp. has the lowest AUC value (approximately 0.699). The AUC value of the mimicry marker (a marker formed by the combination of four single bacterial species) (approximately 0.952) is higher than that of the single bacterial species.

[0043] As can be seen from the above, one or more of the four newly discovered bacterial species in this application can be used as detection variables, and they all have high specificity and sensitivity. Moreover, the AUC of the four microbial markers is greater than 65%. Therefore, one or more of the four microbial markers can be used as detection markers for the diagnosis of patients with colorectal polyps.

[0044] In Table 2, the mimicry markers are marker combinations composed of the four single bacterial species described in this application. Composition 1 is a marker combination composed of Clostridium ramosum, Clostridium bolteae, and Alloprevotella rava; Composition 2 is a marker combination composed of Clostridium bolteae, Alloprevotella rava, and Veillonella dispar; Composition 3 is a marker combination composed of Clostridium bolteae and Clostridium ramosum; and Composition 4 is a marker combination composed of Alloprevotella rava and Veillonella dispar.

[0045] Example 5: Establishing a Logistic Regression Model

[0046] Based on the microbial biomarkers selected above and the relative abundance value of each metabolic biomarker, the binary logistic regression algorithm in RStudio software was used to calculate the first disease probability (also known as the logarithm of dominance y) for each sample. Then, the disease probability optimization formula Z=exp(y) / {1+exp(y)} was used to calculate the disease probability Z of the sample under test. Finally, this disease probability Z was compared with the actual disease status (e.g., severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically: a. Establishing a model Based on the biomarkers identified above, and considering the proportion of colorectal polyps and patients in the training set, the relative abundance of the four detected bacterial species was further used as a single variable. The linear relationship between the relative abundance of the four single bacteria and the disease probability of the samples was then discussed. A binary logistic regression equation was used to calculate the logarithm y (also known as the first probability value y) of the test subject. y=A+B1×x1+B2×x2+B3×x3+B4×x4; Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Alloprevotella rava, x2 is the relative abundance value of Clostridium bolteae, x3 is the relative abundance value of Clostridium ramosum, and x4 is the relative abundance value of Veillonella dispar.

[0047] b. Determine the values ​​of A and B1 to B6 above. After statistical analysis of the sample data, A was 1.2892, B1 was -11.3146, B2 was -178.065, B3 was -92.9715, and B4 was -88.1898. At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is: y=1.2892-11.3146×X1-178.065×X2-92.9715×X3-88.1898×X4; c. Calculate the disease probability Z of the subjects to be tested. Substitute the first probability value y into the following formula to calculate the probability Z that the subject is a patient: Z = exp(y) / {1 + exp(y)}; where Z is the probability value that the subject is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0048] After processing, the formula for calculating the probability Z of disease is: ; d. Validation set data calculation and statistical analysis Based on the validation set data, the relative abundance of each single bacterial species in the disease group and the healthy group for each sample was obtained. Then, the first probability value y was obtained using the aforementioned binary logistic regression method. The probability Z of the test sample being a patient was then calculated using the formula. The results are shown in Tables 3 and 4, where "patient" refers to a patient with irritable bowel syndrome.

[0049] Table 4 shows the mean and standard deviation of the relative abundance of each bacterial species. The mean relative abundance determines the central location 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 data of validation set markers

[0051]

[0052]

[0053]

[0054]

[0055] Table 4. Statistical data on the abundance of biomarkers in the validation set. .

[0056] Note: e represents a power of 10. For example, 7.39713361073e-05 means 7.39713361073 * 10^ ... -5 Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similarly defined.

[0057] It should be noted that the predicted probabilities of individual samples in Table 3 deviate from the clinical labels. This is a normal statistical phenomenon under continuous probability output by the logistic regression model. Especially when the samples are located in the overlapping area of ​​disease and health status or are affected by factors such as sampling timing, dietary fluctuations, and instantaneous changes in the gut microbiota, the model judgment is not necessarily completely consistent with the gold standard label. This is difficult to completely avoid in cohort cross-validation. Other methods can be used for subsequent detection, such as blood routine tests and diagnostic physical signs.

[0058] e. Results and Analysis Based on the results of Examples 4 and 5, the AUC value of the mimicry marker (a marker formed by the combination of four single bacterial species) (approximately 0.952) is higher than that of a single bacterium. The AUC values ​​of the four microbial markers identified in this application are all greater than 70%. One or more of these four microbial markers can be used as diagnostic markers for patients with colorectal polyps. Furthermore, since this invention only requires the collection of stool samples from the test subject, it is not only completely non-invasive but also highly accurate, and can be used to diagnose whether the test subject (stool sample) has colorectal polyps.

[0059] Based on the results obtained from the description in Table 3 above and the calculation formula for the probability of disease Z, it can be seen that the calculation formula for calculating the probability of disease in the sample to be tested, which is summarized in this application, is basically correct and can be used to diagnose the risk and probability of disease in the sample to be tested. The probability of disease in Table 3 above may not fully meet the diagnostic criteria. This is because the intestinal sample of the person to be tested may produce false positive or false negative results. Further testing using other methods is required, including blood routine tests, diagnostic physical signs, etc.

[0060] Example 6: Examination and verification of the disease probability in patients with colorectal polyps and patients with colorectal polyps

[0061] Based on the product and method of Example 5, the disease probability of healthy individuals and patients with colorectal polyps was examined and verified. The specific steps are as follows: S1. Collect intestinal samples from the individuals to be tested and detect the relative abundance of each individual strain in the intestine; wherein, the individual strains include Clostridium bolteae, Alloprevotella rava, Clostridium ramosum, and Veillonella dispar.

[0062] S2. Calculate the logarithm y of the strength of the object under test based on the binary logistic regression equation; y=A+B1×x1+B2×x2+B3×x3+B4×x4; Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Alloprevotella rava, x2 is the relative abundance value of Clostridium bolteae, x3 is the relative abundance value of Clostridium ramosum, and x4 is the relative abundance value of Veillonella dispar.

[0063] S3. Calculate the probability Z of colorectal polyps in the subject of the test based on y, 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: : S4. Based on the comparison between the patient's probability Z-value and the reference value, diagnose or predict the risk of the subject having colorectal polyps.

[0064] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has colorectal polyps; a Z-score less than 0.5 indicates a lower probability; and a Z-score of 0.5 suggests the subject may be a patient with colorectal polyps or a patient with colorectal polyps, requiring further testing using other methods such as mental health testing and drug testing. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to utilize additional testing methods.

[0065] It should be noted that although this embodiment only lists the methods and means of quantitative detection of the sample to be tested through relative abundance value, other means of quantitative detection of microorganisms are also feasible for the present invention (such as absolute abundance or total microbial load information, etc.), and can also be used to assist in the diagnosis of whether the sample to be tested has colorectal polyps. People can choose the appropriate microbial quantitative detection means according to their own needs, which will not be elaborated here.

[0066] Example 7: Detection Reagent

[0067] Based on the description of embodiments 1 to 5 above, it can be seen that the biomarkers selected in this application have good predictive effects. Medical staff can use a single bacterial species as a biomarker to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has colorectal polyps. Medical staff can also combine multiple single bacterial species together as biomarkers to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has colorectal polyps.

[0068] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing colorectal polyps to diagnose whether a sample to be tested has colorectal polyps; at the same time, the microbial markers can be selected from the four single bacterial species related to colorectal polyps discovered in this application, that is, the microbial markers in the detection reagent can include one or more of Alloprevotella rava, Clostridium botulinum, Clostridium ramosum, and Veillonella dispar.

[0069] Example 8: Reagent Kit

[0070] This embodiment also provides a kit that may contain the detection reagent described in Example 7 to diagnose whether a sample to be tested has colorectal polyps. The limitations and technical solutions of the kit in this application can be found in the description of Example 7 above, and will not be repeated here. Similarly, the above kit can also be used in the preparation of products for detecting colorectal polyps, and will not be repeated here.

[0071] Example 9: Products for diagnosing colorectal polyps

[0072] This application also provides a product for diagnosing colorectal polyps, to diagnose whether a sample to be tested has colorectal polyps; the product is specific to one or more of the four single bacterial species found in this application that are associated with colorectal polyps; the product includes primers, probes, antibodies, test strips, aptamers or chips, wherein primers, probes and antibodies can be used to make reagents and kits, which are conventional methods in the art and will not be described in detail here.

[0073] As can be seen from the descriptions of Examples 1-5 and Example 7, when one or more of the four bacterial species described in this application are used as detection markers (i.e., microbial markers) to diagnose whether a person under test has colorectal polyps, it is a technical solution that those skilled in the art can implement to produce corresponding reagents, test strips, aptamers, and chips. That is, reagents, test strips, aptamers, and chips can also be used to assist in the diagnosis of whether a person under test has colorectal polyps. People can choose appropriate products according to their own needs, which will not be elaborated here.

[0074] The method for diagnosing colorectal polyps is completely non-invasive and highly accurate. Using the four newly discovered single bacterial species as microbial biomarkers, the development of corresponding, specific products (primers, probes, antibodies, aptamers, or chips, etc.) should be feasible for those skilled in the art and will not be elaborated upon here.

[0075] Example 10: Diagnosing whether a test sample is from a patient with colorectal polyps

[0076] If it is necessary to diagnose whether a person to be tested has colorectal polyps, in addition to conventional testing methods such as ultrasound examination, serum liver enzyme test, and liver tissue biopsy, medical staff can also use the methods or products described in Examples 1 to 9 above to diagnose the person to be tested, in order to assist medical staff in making a more accurate judgment: (1) If, after continuous observation over multiple time periods, one or more of Alloprevotella rava, Clostridium bolteae, Clostridium ramosum, and Veillonella dispar are found to be significantly reduced, or even show a clear decreasing trend, then the person being tested is more likely to be a patient with colorectal polyps.

[0077] (2) If, after observation over multiple consecutive periods, the person being tested does not exhibit the patterns described in (1) and (2) above, then medical staff can combine [the above information with further details]. Figure 1 According to Table 4, a preliminary judgment is made on whether the person to be tested is a patient with colorectal polyps; (3) If medical staff want to more accurately determine whether the person to be tested is a colorectal polyp patient, they can calculate the probability that the person to be tested is a colorectal polyp patient based on the quantitative detection results of four single bacterial species (such as relative abundance values) and the technical solutions described in Examples 5 to 7.

[0078] Conclusion and explanation: 1. By Figures 1-3 As shown in Table 2, any one of the four newly discovered single bacterial species in this application can serve as a microbial marker for colorectal polyps. Each single bacterial species exhibits both sensitivity and specificity for colorectal polyps. Therefore, the microbial marker for colorectal polyps can be selected from any one or more of the four newly discovered single bacterial species in this application. Specifically: Microbial markers for colorectal polyps can be selected from one or more of Alloprevotella rava, Clostridium bolteae, Clostridium ramosum, and Veillonella dispar.

[0079] 2. As shown in Table 3, it is normal for only one or a few species of bacteria to be detected when testing intestinal samples. This is because there are individual differences. The probability of disease Z in this application is calculated. Therefore, even if a sample contains only a single species of bacteria, this application can still calculate the probability that the sample to be tested has colorectal polyps.

[0080] 3. Predictive effect: The AUC value of the mimicry marker (a marker formed by the combination of 4 single bacterial species) (approximately 0.983) is higher than that of a single bacterial species. When other microorganisms are added to one or several single bacterial species for testing, the test result (AUC value) will not be lower than the lowest AUC value of multiple single bacterial species.

[0081] 4. The four newly discovered bacterial species in this application can all be used as detection variables. They all have high specificity and sensitivity, and the AUC of the four microbial markers is greater than 70%. One or more of the four microbial markers can be used as detection markers for the diagnosis of patients with colorectal polyps.

[0082] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. The application of a reagent for quantitative detection of microbial markers in the preparation of products for diagnosing colorectal polyps, characterized in that, The microbial markers include Clostridium botulinum bolteae and Alloprevotella rava, which are significantly increased in patients with colorectal polyps.

2. The application according to claim 1, characterized in that, The microbial markers also include Veillonella dispar or Clostridium ramosum, with Veillonella dispar being significantly reduced in patients with colorectal polyps and Clostridium ramosum being significantly increased in patients with colorectal polyps.

3. The application according to claim 1 or 2, characterized in that, The microbial markers were a combination of Clostridium bolteae, Alloprevotella rava, Clostridium ramosum, and Veillonella dispar. Clostridium bolteae, Clostridium ramosum, and Alloprevotellarava were significantly increased in the colorectal polyp group, while Veillonella dispar was significantly decreased in the colorectal polyp group.

4. The application according to claim 3, characterized in that, The reagents include primers and / or probes.

5. The application according to claim 4, characterized in that, The products include one or more of the following: reagent kits, chips, and diagnostic systems.

6. A gut microbiota marker for diagnosing or predicting colorectal polyps, characterized in that, The microbial markers include Clostridium bolteae, Alloprevotella rava, Clostridium ramosum, and Veillonella dispar.

7. A gut microbiota marker for diagnosing or predicting colorectal polyps, characterized in that, The gut microbiota marker is any one of the following combinations: a) The microbial markers are a combination of markers consisting of Clostridium ramosum, Clostridium botleii, and Alloprevotella rava; b) The microbial markers are a combination of markers consisting of Alloprevotella rava, Clostridium bolteae, Clostridium ramosum, and Veillonella dispar.

8. A product for diagnosing or predicting colorectal polyps, characterized in that: The product includes one or more of reagents, test strips, aptamers, and chips, and the product is specific to the intestinal microbial biomarkers of claim 6 or 7 and is used for quantitative detection of the intestinal microbial biomarkers of claim 6 or 7.

9. A kit for diagnosing or predicting colorectal polyps, characterized in that: The kit contains reagents for the quantitative detection of the microbial markers of claim 6 or 7.

10. The use of the kit of claim 9 in the preparation of a product for diagnosing colorectal polyps, wherein Clostridium botulinum, Clostridium ramosum, and Alloprevotellarava showed a significant increase in the colorectal polyp group, and Veillonella dispar showed a significant decrease in the colorectal polyp group.

Images