Microbial markers of colorectal cancer and uses thereof

Abstract

The application discloses a colorectal cancer microbial marker and application thereof, and belongs to the field of biological medicine. The microbial marker comprises one or more of Eubacterium siraeum, Roseburia intestinalis, Haemophilus parainfluenzae and Eubacterium rectale. The application provides a product (such as a kit) comprising detection reagents for detecting the relative abundance of the microbial marker. The product provided by the application has good feasibility and accuracy, can effectively evaluate whether a teenager has a risk of colorectal cancer, and provides a new tool for clinical diagnosis.

Description

Technical Field

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

[0002] Currently, the diagnosis of colorectal cancer (CRC) mainly relies on colonoscopy (the gold standard, which is invasive and has low compliance) and fecal occult blood test (FOBT / FIT, which is non-invasive but has insufficient sensitivity, with a detection rate of only 25%–40% for advanced adenomas). There is an urgent need to develop a new non-invasive, convenient, and highly sensitive method.

[0003] In recent years, gut microbiota testing, as a non-invasive method, has shown potential in early warning through fecal microbial analysis, and is expected to make up for the shortcomings of existing technologies. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a colorectal cancer microbial biomarker, Dede, and its application, which can offer a new approach and method for diagnosing colorectal cancer.

[0005] 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 cancer. The microbial markers are a combination of inert eubacterium siraeum, intestinal Roseburia intestinalis, Haemophilus parainfluenzae, and rectal eubacterium rectale, which are significantly reduced in the colorectal cancer group.

[0006] In a second aspect, a gut microbial biomarker for diagnosing or predicting colorectal cancer is provided, said microbial biomarker being a combination of biomarkers consisting of *Eubacterium siraeum*, *Roseburia intestinalis*, *Haemophilus parainfluenzae*, and *Eubacterium rectale*.

[0007] Thirdly, a product for diagnosing or predicting colorectal cancer 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.

[0008] Fourthly, a kit for diagnosing or predicting colorectal cancer is provided, the kit containing reagents for quantitative detection of the microbial markers.

[0009] Fifthly, the kit described in the fourth aspect is provided for use in the preparation of products for diagnosing colorectal cancer, wherein the inert eubacterium siraeum, enteric Roseburia intestinalis, Haemophilus parainfluenzae, and rectal eubacterium rectale are significantly reduced in the colorectal cancer group.

[0010] It should be noted that this invention newly discovered and verified a strong correlation between the aforementioned gut microbiota and colorectal cancer, which can be used to diagnose whether a person under testing has colorectal cancer. 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 cancer. Users can choose according to their own needs, and these will not be elaborated upon here.

[0011] The beneficial effects of this invention are as follows: 1. This invention newly discovers four intestinal microorganisms associated with colorectal cancer, including: Roseburia intestinalis, Eubacterium siraeum, Haemophilus parainfluenzae, and Haemophilus parainfluenzae.

[0012] After research and verification, it was found that for colorectal cancer patients, one or more of the above four types of intestinal microorganisms can be used to diagnose whether the person being tested has colorectal cancer.

[0013] 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 cancer. It is completely non-invasive and highly accurate.

[0014] Metagenomic sequencing provides higher resolution, enabling the analysis of microbial communities to penetrate to the species or 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.

[0015] 3. This invention also provides a product for diagnosing colorectal cancer. This product can diagnose the risk of colorectal cancer in a test subject 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 colorectal cancer in a test subject's (fecal sample), with relatively accurate assessment results. Attached Figure Description

[0016] 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 in colorectal cancer patients and healthy individuals; Figure 3 The ROC curve for the prediction score of the validation set. Detailed Implementation

[0017] 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.

[0018] This invention provides a microbial biomarker associated with colorectal cancer and its application. The detection results are more accurate and representative, and it can be used as a predictive factor for colorectal cancer (including T1DM and T2DM). It can be used to diagnose whether a person being tested has colorectal cancer. Specifically, the technical concept of this invention is as follows: To address the clinical needs for the diagnosis and detection of colorectal cancer, this invention collects samples from colorectal cancer patients and healthy individuals, and through standardized experimental testing procedures (specific experimental methods are described in Examples 1-3), screens out four microorganisms associated with colorectal cancer, including: *Eubacterium siraeum*, *Roseburia intestinalis*, *Haemophilus parainfluenzae*, and *Eubacterium rectale*.

[0019] ROC curve analysis showed that the above four biomarkers have high specificity and sensitivity as detection variables, and these four bacterial species can be used as detection biomarkers for the prediction and diagnosis of colorectal cancer patients.

[0020] 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.

[0021] Example 1: Sample Collection The inclusion criteria for stool samples from colorectal cancer patients are as follows: 1. Over 40 years of age; 2. Clinically diagnosed with colorectal cancer; 3. Stable vital signs.

[0022] The exclusion criteria for stool samples in the colorectal cancer group were as follows: 1. Received colorectal cancer treatment within 1 month; 2. Took antibiotics, probiotics, or prebiotics within the past 3 months; 3. Had any chronic disease, including neurobehavioral disorders; 4. Received medications affecting gastrointestinal motility within 1 week; 5. Had a history of functional dyspepsia, aerophagia, or abdominal migraine pain; 6. Showed growth retardation; 7. Had gastrointestinal obstruction or stricture; 8. Had a history of abdominal surgery, peptic ulcer, or inflammatory bowel disease; 9. The researchers deemed the patient unsuitable for inclusion in this study.

[0023] The inclusion criteria for fecal samples from the control group (healthy individuals) were as follows: 1. Over 40 years of age; 2. No sleep disorders or other neurological diseases; 3. No diabetes or other metabolic diseases; 4. No colorectal cancer 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.

[0024] The exclusion criteria for stool samples from the control group (healthy individuals) were the same as those for the colorectal cancer group.

[0025] The fecal samples used in this invention were collected from Hubei Province. For both colorectal cancer patients and healthy individuals, written informed consent was obtained from both the patients and their legal guardians (if any). Based on the aforementioned inclusion and exclusion criteria, a total of 123 colorectal cancer patients and 142 healthy individuals were collected in this invention. The statistical results are as follows: Table 1 Sample Information Table .

[0026] Example 2: DNA extraction, library construction, and sequencing 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0027] 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).

[0028] 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).

[0029] 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.

[0030] Example 3: LEfSe analysis for screening microbial biomarkers 1. Split the dataset 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 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 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 cancer group, including Eubacterium siraeum, Roseburia intestinalis, Haemophilus parainfluenzae, and Eubacterium rectale.

[0031] Example 4: Verifying the reliability of the above four microbial biomarkers 1. First, the remaining 20% ​​of the participants in Example 1 (including those in the colorectal cancer 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 function (ROC curve) was analyzed to obtain the cutoff value (optimal cutoff value).

[0032] 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) 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.

[0033] 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.

[0034] Table 2 ROC diagnostic curve results .

[0035] As shown in Table 2, for single bacterial species, the AUC value of Rosbyella esculenta was the highest (approximately 0.935), while the AUC value of Haemophilus parainfluenzae was the lowest (approximately 0.715). The AUC value of the mimicry marker (a marker formed by the combination of four single bacterial species) (approximately 0.970) was higher than that of the single bacterial species.

[0036] 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 70%. Therefore, one or more of the four microbial markers can be used as detection markers for the diagnosis of colorectal cancer patients.

[0037] refer to Figure 3As described in Table 2, composition 1 is a biomarker combination composed of *Roseburia intestinalis*, *Eubacterium rectale*, and *Eubacterium siraeum*; composition 2 is a biomarker combination composed of *Eubacterium rectale*, *Eubacterium siraeum*, and *Haemophilus parainfluenzae*; composition 3 is a biomarker combination composed of *Roseburia intestinalis* and *Eubacterium rectale*; and composition 4 is a biomarker combination composed of *Eubacterium siraeum* and *Haemophilus parainfluenzae*.

[0038] Example 5: Establishing a Logistic Regression Model 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 the proportion of colorectal cancer patients in the training set, the relative abundance values ​​of the four detected bacterial species were further used as single variables. The linear relationship between the relative abundance values ​​of the four single bacteria and the probability of disease in the samples was then discussed. The logarithm y (also known as the first probability value y) of the dominance of the test subject was calculated using a 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 Eubacterium siraeum, x2 is the relative abundance value of Roseburia intestinalis, x3 is the relative abundance value of Haemophilus parainfluenzae, and x4 is the relative abundance value of Eubacterium rectale.

[0039] b. Determine the values ​​of A and B1 to B4 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.

[0040] 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.

[0041] Table 4 shows the relative abundance mean and standard deviation for each bacterial species. The relative abundance mean determines the central location of the data distribution, while the standard deviation reflects the dispersion of the data relative to the mean. The p-value is a statistic calculated using the rank-sum test formula. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0042] Table 3. Relevant data on validation set markers

[0043]

[0044] .

[0045] Table 4. Statistical data on the abundance and prevalence of biomarkers in the validation set. .

[0046] Note: e represents a power of 10. For example, 7.39713361073e-05 means 7.39713361073 * 10^ ... -5Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similarly defined.

[0047] It should be noted that the mimicry markers in this application are marker combinations formed by a combination of four single bacterial species. The disease probability in Table 3 above is the sample disease probability obtained by each sample based on its own collected relative abundance value and the above disease probability Z calculation formula. For example, in Table 4, "0.713554332" means that the average disease probability of the colorectal cancer group is 0.713554332, and "0.163347747" means that the average disease probability of the healthy group is 0.163347747. The standard deviation is similar and will not be repeated.

[0048] A Z-score greater than 0.5 indicates a higher probability of the subject having colorectal cancer; a Z-score less than 0.5 indicates a lower probability; a Z-score of 0.5 suggests the subject may be a patient or have colorectal cancer, requiring further testing using methods such as mental health assessments and drug testing. Furthermore, the closer the Z-score is to 0.5, the more necessary additional testing methods become.

[0049] It should also be noted that Table 4 above shows the mean and standard deviation of the relative abundance values ​​of the four single bacterial species, as well as the mean and standard deviation of the disease probability of the mimicry markers. Among them, the disease probability of the mimicry markers is obtained by calculating the disease probability values ​​of the data samples in the validation set in Table 3 (mean calculation and squared difference calculation).

[0050] 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 0.970) 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 colorectal cancer patients. 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 cancer.

[0051] 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.

[0052] Example 6: Examining and verifying the incidence rate of colorectal cancer in a concentrated area.

[0053] Based on the product and method of Example 5, the incidence rate of colorectal cancer was examined and verified in a group of healthy individuals and colorectal cancer patients. The specific steps are as follows: S1. Collect intestinal samples from the individuals to be tested and detect the relative abundance of each individual bacterial strain in the intestine; wherein, the individual bacterial strains include Eubacterium siraeum, Roseburia intestinalis, Haemophilus parainfluenzae, and Eubacterium rectale.

[0054] 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 Eubacterium siraeum, x2 is the relative abundance value of Roseburia intestinalis, x3 is the relative abundance value of Haemophilus parainfluenzae, and x4 is the relative abundance value of Eubacterium rectale.

[0055] S3. Calculate the probability Z of the subject being a colorectal cancer patient 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 cancer.

[0056] In practice, a Z-score greater than 0.5 indicates a higher probability of the subject having colorectal cancer; 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 or have colorectal cancer, requiring further investigation 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.

[0057] 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 cancer. People can choose the appropriate microbial quantitative detection means according to their own needs, which will not be elaborated here.

[0058] Example 7: Detection Reagent

[0059] 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 cancer. 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 cancer.

[0060] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing colorectal cancer to diagnose whether a sample to be tested has colorectal cancer; at the same time, the microbial markers can be selected from the four single bacterial species related to colorectal cancer discovered in this application, that is, the microbial markers in the detection reagent can include one or more of the following: Eubacterium siraeum, Roseburia intestinalis, Haemophilus parainfluenzae, and Eubacterium rectale.

[0061] Example 8: Reagent Kit and Product This embodiment also provides a kit that may contain the detection reagent described in Embodiment 7 to diagnose whether a sample to be tested has colorectal cancer. The limitations and technical solutions of this application regarding the kit can be found in the description of Embodiment 7 above, and will not be repeated here. Similarly, the above kit can also be used in the preparation of products for detecting colorectal cancer, and will not be repeated here.

[0062] This application also provides a product for diagnosing colorectal cancer, to diagnose whether a sample to be tested has colorectal cancer; the product is specific to one or more of the four single bacterial species found in this application that are associated with colorectal cancer, and 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 is a conventional method in the art and will not be described in detail here.

[0063] 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 cancer, 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 cancer. People can choose appropriate products according to their own needs, which will not be elaborated here.

[0064] The method for diagnosing colorectal cancer in individuals 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.

[0065] Example 9: Diagnosing whether a test sample belongs to a colorectal cancer patient

[0066] If it is necessary to diagnose whether a person to be tested has colorectal cancer, 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 8 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 the following bacteria are found to be significantly reduced, or even showing a significant decreasing trend, the person being tested is more likely to be a colorectal cancer patient.

[0067] (2) If, after observation over multiple consecutive periods, the person being tested does not exhibit the pattern described in (1) above, then medical staff can combine [the above information with further details]. Figure 1 Table 4 and the results will be used to make a probabilistic judgment on whether the person being tested is a colorectal cancer patient; (3) If medical staff want to more accurately determine whether the person to be tested is a colorectal cancer patient, they can calculate the probability that the person to be tested is a colorectal cancer 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.

[0068] Conclusion and explanation: 1. By Figures 1-3As shown in Table 2, any one of the four newly discovered single bacterial species in this application can be used as a microbial biomarker for colorectal cancer. Each single bacterial species has both sensitivity and specificity for colorectal cancer. Therefore, the microbial biomarker for colorectal cancer can be selected from any one or more of the four newly discovered single bacterial species in this application. Specifically: Microbial markers for colorectal cancer can be selected from one or more of the following: Eubacterium siraeum, Roseburia intestinalis, Haemophilus parainfluenzae, and Eubacterium rectale.

[0069] 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 cancer.

[0070] 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.

[0071] 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 colorectal cancer patients.

[0072] 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 cancer, characterized in that, The microbial markers are a combination of inert eubacterium siraeum, intestinal Roseburia intestinalis, Haemophilus parainfluenzae, and rectal eubacterium rectale. These inert eubacterium siraeum, intestinal Roseburia intestinalis, Haemophilus parainfluenzae, and rectal eubacterium rectale were significantly reduced in the colorectal cancer group.

2. The application according to claim 1, characterized in that, The reagents include primers and / or probes.

3. A gut microbiota marker for diagnosing or predicting colorectal cancer, characterized in that, The microbial markers are a combination of markers consisting of inert eubacterium siraeum, enteric roseburia intestinalis, Haemophilus parainfluenzae, and rectal eubacterium rectale.

4. A product for diagnosing or predicting colorectal cancer, 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 3 and is used for quantitative detection of the intestinal microbial biomarkers of claim 3.

5. A kit for diagnosing or predicting colorectal cancer, characterized in that: The kit contains reagents for the quantitative detection of the microbial markers of claim 3.

6. The use of the kit according to claim 5 in the preparation of a product for diagnosing colorectal cancer, wherein the inert Eubacterium siraeum, Roseburia intestinalis, Haemophilus parainfluenzae, and Eubacterium rectale are significantly reduced in the colorectal cancer group.

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