Microflora marker for detecting tumor radiotherapy sensitivity and application thereof

Abstract

The invention provides a flora marker for detecting tumor radiotherapy sensitivity and application of the flora marker, and belongs to the technical field of tumor radiotherapy sensitivity diagnosis. According to the present invention, the tumor radiotherapy sensitivity of the cancer patient can be effectively distinguished by using the flora markers, such as sRoseburia inulinaria, sPrevotella, sAnerostichopus, sClostridioidedifficile, sPrevotella, sSchaaliodontia, sAlistichopus, and the like, and the method can further be used for the grading identification of the tumor radiotherapy sensitivity of the cancer patient so as to improve the accuracy of the determination of the radiotherapy sensitivity degree of the cancer patient.

Description

Technical Field

[0001] The present invention belongs to the technical field of tumor radiotherapy sensitivity diagnosis, and in particular relates to a microbial marker for detecting tumor radiotherapy sensitivity and an application thereof. Background Art

[0002] Colorectal cancer accounts for approximately 10% of all cancers and cancer-related deaths diagnosed worldwide each year. It is the second most common cancer in women and the third most common cancer in men. Radiotherapy is an important part of the comprehensive treatment of colorectal cancer, and the preoperative chemoradiotherapy treatment strategy remains the standard treatment strategy for locally advanced rectal cancer (stage II and III). However, while radiotherapy brings therapeutic benefits to patients, it also increases its toxic side effects. The proportion of people who benefit is relatively small, and there are still many problems that need to be solved.

[0003] In the field of radiotherapy, clinical efficacy varies greatly between individuals. Even if the same regimen and dose of radiotherapy are received, about half of the tumors still do not achieve obvious regression, and even progress. The resistance of colorectal cancer (CRC) cells to radiotherapy largely leads to poor clinical prognosis of colorectal cancer patients. However, there are still obstacles in distinguishing colorectal cancer patients with high sensitivity (HS), moderate sensitivity (MS) and low sensitivity (LS) to radiotherapy. In the evaluation of radiotherapy response, tumor regression grading (TRG) is widely accepted. Previous studies have shown that AJCC-TRG grading accurately predicts the prognosis of rectal cancer after chemoradiotherapy. However, TRG grading fails to grade the sensitivity of radiation response of colorectal cancer patients before treatment. Therefore, it is of great significance to obtain a method for screening tumor radiotherapy sensitivity markers for the prediction of radiotherapy sensitivity. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a microbial marker for detecting tumor radiotherapy sensitivity and its application. The present invention utilizes the microbial marker to effectively detect the patient's sensitivity to tumor radiotherapy.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] The present invention provides a microbial marker for detecting tumor radiotherapy sensitivity, wherein the microbial marker comprises one or more of s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__bacterium_New, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, s__Anaerostipes_unclassified, and s__Prevotella_stercorea.

[0007] The present invention also provides an application of the above-mentioned microbial marker in the preparation of a product for diagnosing tumor radiotherapy sensitivity.

[0008] The present invention also provides an application of the above-mentioned microbial marker in the preparation of a product for diagnosing tumor radiotherapy sensitivity grading.

[0009] Preferably, the tumor radiotherapy sensitivity is graded into highly sensitive tumor radiotherapy and moderately sensitive tumor radiotherapy.

[0010] The present invention also provides a system for predicting tumor radiotherapy sensitivity using the above-mentioned microbiota markers, comprising a nucleic acid sample separation unit for separating a fecal microbiota nucleic acid sample from a test sample;

[0011] A data processing unit, using the above-mentioned microbial markers to establish a logistic regression model and obtain a critical value;

[0012] The result determination unit is used to compare the critical value obtained by the data processing unit with the set diagnosis value.

[0013] Preferably, the logistic regression model is a first logistic regression model and a second logistic regression model;

[0014] The first logistic regression model is a logistic regression model constructed for the stool sample to be tested vs. the stool sample of the colorectal cancer patient who is not sensitive to radiotherapy. When the critical value is <0.62, it indicates that the tumor patient is not sensitive to radiotherapy. When the critical value is ≥0.62, it indicates that the tumor patient is moderately sensitive to radiotherapy or highly sensitive to radiotherapy.

[0015] The second logistic regression model is a logistic regression model constructed by comparing the stool samples to be tested with a critical value ≥ 0.62 with the stool samples of colorectal cancer patients with moderate sensitivity to radiotherapy. When the critical value is < 0.48, it indicates that the tumor patient is moderately sensitive to radiotherapy, and when the critical value is ≥ 0.48, it indicates that the tumor patient is highly sensitive to radiotherapy.

[0016] Preferably, when the first logistic regression model is established using the above-mentioned microbial markers, the microbial markers are s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, s__Anaerostipes_unclassified, and s__Prevotella_stercorea.

[0017] Preferably, when the second logistic regression model is established using the above-mentioned microbial markers, the microbial markers are s__Roseburia_inulinivorans, s__Prevotella_stercorea, s__Anaerostipes_unclassified, s__Clostridioides_difficile, s__Prevotella_nigrescens, s__Schaalia_odontolytica and s__Alistipes_obesi.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention provides a microbial marker for detecting tumor radiotherapy sensitivity and its application. The present invention can effectively distinguish the tumor radiotherapy sensitivity of cancer patients by using the microbial marker, and can also be used for grading and identifying the tumor radiotherapy sensitivity of cancer patients, thereby improving the accuracy of judging the radiotherapy sensitivity of cancer patients and providing an important theoretical basis for efficiently using radiotherapy to treat cancer patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the 16S rDNA region of bacteria;

[0021] Figure 2 The principle of PacBio sequencing;

[0022] Figure 3 It is the PacBioCCS model;

[0023] Figure 4 This is the full-length 16S experimental process;

[0024] Figure 5 To draw the ROC curve according to the method of fitting the logistic regression model, A is the ROC curve of the stool sample to be tested vs the stool sample of the colorectal cancer patient who is not sensitive to radiotherapy; B is the ROC curve of the stool sample to be tested vs the stool sample of the colorectal cancer patient who is moderately sensitive to radiotherapy with P ≥ 0.62;

[0025] Figure 6 To draw the ROC curve according to the microbiota involved in the logistic regression model equation, A is the ROC curve of the microbiota involved in the fecal sample to be tested vs. the fecal sample of the colorectal cancer patient who is not sensitive to radiotherapy detected by using the microbiota marker in Example 2; B is the ROC curve of the microbiota involved in the fecal sample to be tested with P≥0.62 vs. the fecal sample of the colorectal cancer patient who is moderately sensitive to radiotherapy detected by using the microbiota marker in Example 2. DETAILED DESCRIPTION

[0026] The present invention provides a microbial marker for detecting tumor radiotherapy sensitivity, wherein the microbial marker comprises one or more of s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__bacterium_New, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, s__Anaerostipes_unclassified, and s__Prevotella_stercorea.

[0027] In the present invention, the microbiota markers are preferably s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, s__Anaerostipes_unclassified and s__Prevotella_stercorea. The present invention uses the above 11 microbiota markers to effectively diagnose whether a patient is sensitive to tumor radiotherapy. Furthermore, after the above-mentioned 11 microbial markers are used to effectively diagnose patients who are sensitive to tumor radiotherapy, in order to determine the degree of sensitivity of the above-mentioned patients to tumor radiotherapy, the microbial markers used are preferably s__Roseburia_inulinivorans, s__Prevotella_stercorea, s__Anaerostipes_unclassified, s__Clostridioides_difficile, s__Prevotella_nigrescens, s__Schaalia_odontolytica and s__Alistipes_obesi. The present invention uses these 7 microbial markers to effectively diagnose whether the patient is highly sensitive to tumor radiotherapy or moderately sensitive to tumor radiotherapy.

[0028] Based on this, the present invention also provides an application of the above-mentioned microbial marker in the preparation of a product for diagnosing tumor radiotherapy sensitivity.

[0029] The present invention also provides an application of the above-mentioned microbial marker in the preparation of a product for diagnosing tumor radiotherapy sensitivity grading.

[0030] In the present invention, the tumor radiotherapy sensitivity is graded into high sensitivity to tumor radiotherapy and moderate sensitivity to tumor radiotherapy. The present invention divides tumor radiotherapy sensitivity into high sensitivity to tumor radiotherapy, moderate sensitivity to tumor radiotherapy and low sensitivity to tumor radiotherapy (also known as insensitive to tumor radiotherapy) according to the NAR (Neoadjuvant Rectal) score. When the NAR score is <8, it means that the patient is highly sensitive to tumor radiotherapy, when the NAR score is 8-16, it means that the patient is moderately sensitive to tumor radiotherapy, and when the NAR score is >16, it means that the patient is insensitive to tumor radiotherapy.

[0031] The present invention also provides a system for predicting tumor radiotherapy sensitivity using the above-mentioned microbiota markers, comprising a nucleic acid sample separation unit for separating a fecal microbiota nucleic acid sample from a test sample;

[0032] A data processing unit, using the above-mentioned microbial markers to establish a logistic regression model and obtain a critical value;

[0033] The result determination unit is used to compare the critical value obtained by the data processing unit with the set diagnosis value.

[0034] In the present invention, the critical value is also called the optimal threshold for predicting P. In the present invention, the logistic regression model is preferably a first logistic regression model and a second logistic regression model. When judging whether the stool sample to be tested is sensitive to tumor radiotherapy, the diagnostic value is 0.62, and when judging that the stool sample to be tested is sensitive to tumor radiotherapy, and further judging its sensitivity to tumor radiotherapy, the diagnostic value is 0.48.

[0035] In the present invention, the first logistic regression model is a logistic regression model constructed by comparing the stool sample to be tested with the stool sample of the colorectal cancer patient who is not sensitive to radiotherapy. When the critical value is <0.62, it indicates that the tumor patient is not sensitive to radiotherapy. When the critical value is ≥0.62, it indicates that the tumor patient is moderately sensitive to radiotherapy or highly sensitive to radiotherapy.

[0036] The second logistic regression model is a logistic regression model constructed by comparing the stool samples to be tested with a critical value ≥ 0.62 with the stool samples of colorectal cancer patients with moderate sensitivity to radiotherapy. When the critical value is < 0.48, it indicates that the tumor patient is moderately sensitive to radiotherapy, and when the critical value is ≥ 0.48, it indicates that the tumor patient is highly sensitive to radiotherapy.

[0037] In the present invention, when the first logistic regression model is established using the above-mentioned microbial markers, the microbial markers are preferably s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, s__Anaerostipes_unclassified, and s__Prevotella_stercorea.

[0038] In the present invention, when the second logistic regression model is established using the above-mentioned flora markers, the flora markers are preferably s__Roseburia_inulinivorans, s__Prevotella_stercorea, s__Anaerostipes_unclassified, s__Clostridioides_difficile, s__Prevotella_nigrescens, s__Schaalia_odontolytica and s__Alistipes_obesi.

[0039] The present invention utilizes the above-mentioned microbiota markers to effectively diagnose whether a tumor patient is sensitive to tumor radiotherapy and the degree of sensitivity to tumor radiotherapy, and the diagnosis is highly accurate.

[0040] In the present invention, unless otherwise specified, all raw material components are commercially available products well known to those skilled in the art.

[0041] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0042] Example 1

[0043] A method for screening and detecting bacterial flora markers for tumor radiotherapy sensitivity, comprising the following steps:

[0044] 1.1 Fecal DNA extraction

[0045] (1) Add 0.25-0.5 g of sample to a 2 mL centrifuge tube. If it is a liquid sample, transfer 200 μL to the centrifuge tube, add 500 μL buffer SA, 100 μL buffer SC and 0.25 g grinding beads. (There may be residual RNA in the fecal sample. If RNA needs to be removed, it is recommended to add 10 μL RNase A (TIANGEN, RT405-02, self-prepared)) Vortex to mix or use a tissue homogenizer to mix, then heat and lyse at 70°C for 15 minutes to improve lysis efficiency. Centrifuge at 12000 rpm for 1 minute, and transfer the first supernatant (about 500 μL) to a new 2 mL centrifuge tube.

[0046] (2) Add 200 μL of buffer SH to the first supernatant, mix well, vortex for 5 seconds, and place at 4°C for 10 minutes.

[0047] (3) Centrifuge at 12000 rpm for 3 min, transfer the second supernatant to a new 2 mL centrifuge tube, add 500 μL of buffer GFA (please check whether isopropanol has been added before use), and mix by inversion.

[0048] (4) Add 10 μL of magnetic bead suspension G and vortex to mix for 5 min.

[0049] (5) Place the centrifuge tube on the magnetic rack and let it stand for 30 seconds. After the magnetic beads are completely adsorbed, carefully remove the liquid.

[0050] (6) Remove the centrifuge tube from the magnetic rack, add 700 μL of deproteinized solution RD (please check whether anhydrous ethanol has been added before use), and shake to mix for 5 minutes.

[0051] (7) Place the centrifuge tube on the magnetic rack and let it stand for 30 seconds. After the magnetic beads are completely adsorbed, carefully remove the liquid.

[0052] (8) Remove the centrifuge tube from the magnetic rack, add 700 μL of PWD (please check whether anhydrous ethanol has been added before use), and shake to mix for 3 min.

[0053] (9) Place the centrifuge tube on the magnetic rack and let it stand for 30 seconds. After the magnetic beads are completely adsorbed, carefully remove the liquid.

[0054] (10) Repeat steps (8) and (9) once.

[0055] (11) Place the centrifuge tube on a magnetic rack and dry it at room temperature for 5 to 10 minutes.

[0056] (12) Remove the centrifuge tube from the magnetic rack, add 50-100 μL of elution buffer TB, vortex to mix, and incubate at 56°C for 5 min. During this time, vortex to mix three times, 3-5 times each time.

[0057] (13) Place the centrifuge tube on a magnetic rack and let it stand for 2 min. After the magnetic beads are completely adsorbed, carefully transfer the DNA solution to a new centrifuge tube and store it under appropriate conditions to obtain fecal DNA.

[0058] 1.2 Introduction to full-length amplicon

[0059] 16S rDNA is a gene that encodes the ribosome 30S small subunit component (16S rRNA) in prokaryotes. It is about 1500bp in length and includes 9 variable regions (V1-V9) and 10 conserved regions. The conserved regions reflect the relationship between species, while the variable regions reflect the differences between species. The degree of variation is closely related to the bacterial phylogeny and is considered to be the most suitable indicator for bacterial phylogeny and classification identification. Figure 1 .

[0060] Unlike the second-generation amplicons that only target 1-2 hypervariable regions, the third-generation amplicons sequence the full length of 16S (the amplification primer pair is 27F: AGRGTTYGATYMTGGCTCAG (SEQ ID NO.1) and 1492R: RGYTACCTTGTTACGACTT (SEQ ID NO.2)), where R represents A / G, Y represents C / T, and M represents A / C, avoiding the influence of different hypervariable regions and PCR preference. More hypervariable region information can significantly improve the resolution and accuracy of species annotation, and has been widely used in the study of microbial communities in different habitats.

[0061] 1.3 Introduction to Sequencing Principle

[0062] The single molecule real-time (SMRT) technology of PacBio (Pacific BioSciences) has the advantages of long read length and high accuracy; the Sequel system is the current mainstream third-generation sequencing platform. There are a large number of nanoscale holes on the SMRT cell of its sequencing reaction, called zero-mode waveguides (ZMWs). Usually, a template DNA and a molecule of DNA polymerase are fixed in each hole, and the sequence information is determined by detecting the fluorescent signal of dNTPs during the chain extension process. The principle of PacBio sequencing can be found in Figure 2 .

[0063] The Circular Consensus Sequencing (CCS) mode performs multiple sequencing on each template molecule and obtains accurate sequence information through subread correction, namely CCS reads (also called HiFi reads); it is currently mainly used for libraries with insert fragments less than 2kb, which is highly consistent with the requirements of 16S rDNA full-length sequencing. Figure 3 .

[0064] 2. Workflow

[0065] 2.1 Experimental Procedure

[0066] In order to ensure the accuracy and reliability of sequencing data from the source, there are rigorous and reliable sample testing and quality control processes from DNA extraction to sequencing. The sample quality is strictly controlled at every stage to ensure the authenticity and reliability of the sequencing data. Figure 4 .

[0067] 2.2 Information Analysis Process

[0068] After the sequencing is completed, the original data Raw CCS obtained will be quality controlled to obtain high-quality ValidCCS. In order to meet the needs of different customers, two different analysis modes can be provided in the future. One is based on Qiime2's DADA2 (Divisive Amplicon Denoising Algorithm), which obtains representative sequences ASVs (Amplicon Sequence Variants) with single-base accuracy through "dereplication"; the other is to use VSEARCH for OTUs (Operational Taxonomic Units) clustering (97% similarity). At present, the former is more widely recognized and recommended due to its higher phylogenetic resolution and comparability between different data sets. The project uses the ASVs method by default. After obtaining the ASVs / OTUs table, further diversity analysis, species classification annotation and difference analysis are carried out.

[0069] 3. Standard Analysis

[0070] 3.1 Sequencing data quality control statistics

[0071] After sequencing, first obtain the Raw CCS (circular consensus sequencing), and then perform further data quality control to obtain the Valid CCS. The steps are as follows:

[0072] (1) SMRT Links identification to obtain Raw CCS, parameters: minPasses ≥ 3, minPredictedAccuracy ≥ 0.99;

[0073] (2) limma uses default parameters to identify barcode split samples;

[0074] (3) cutadapt identified and removed the primers and then performed length screening to retain reads of 1200-1650 bp;

[0075] (4) The final Valid CCS was denoised and chimeras were filtered using the qiime dada2 denoise-single method to obtain the ASV (feature) feature sequence and abundance table, and singletons ASVs were removed to obtain the processed Valid CCS.

[0076] 3.2 Screening of markers

[0077] The processed Valid CCS is then used to screen markers.

[0078] The processed Valid CCS was classified into 6 categories according to the NAR (Neoadjuvant Rectal) score and before and after radiotherapy: prHS, prMS, prLS, poHS, poMS and poLS. Specifically, prHS stands for sensitive data before radiotherapy (highly sensitive bacterial community subset before radiotherapy), prMS stands for moderately sensitive data before radiotherapy (moderately sensitive bacterial community subset before radiotherapy), prLS stands for insensitive data before radiotherapy (insensitive bacterial community subset before radiotherapy), poHS stands for sensitive data after radiotherapy (highly sensitive bacterial community subset after radiotherapy), poMS stands for moderately sensitive data after radiotherapy (moderately sensitive bacterial community subset after radiotherapy), and poLS stands for insensitive data after radiotherapy (insensitive bacterial community subset after radiotherapy), which were used as the initial sample set.

[0079] Step S1: The initial sample set is processed by principal component analysis and principal coordinate analysis (PCoA) to obtain an initial differential flora sample set; the initial differential flora sample set is preliminarily screened by rank sum test to obtain a second differential flora sample set.

[0080] First, establish the null hypothesis: the two groups of samples come from the same population. Mix the two groups of data and rank them uniformly from small to large. When encountering the same data, take the average rank and calculate the statistic. Set the significance level a=0.05, and obtain the probability of the current statistic value under the condition that the null hypothesis is true. If p-value<0.05, reject the null hypothesis, indicating that there is a significant difference between the two samples.

[0081] Step S2: Based on the logistic regression model and P-value established between the differential samples in the second differential flora sample set and the significant clinical indicators, the differential flora after eliminating the interference of clinical factors is obtained, that is, the third differential flora sample set is obtained.

[0082] The initial clinical indicators were screened using multiple comparisons and PerMANOVA multivariate analysis of variance, and significant clinical indicators were obtained, including:

[0083] a. Obtaining initial clinical indicators:

[0084] A total of 271 clinical samples were collected, and the clinical indicator information was extracted, including treatment response group, age, gender, BMI, smoking, drinking, T, N, GTV, distance from the anus in cm (MR / CT), CRM (MR), EMVI (MR), NLR, concurrent chemotherapy regimen, hormone use, and antibiotic use, a total of 16 items.

[0085] b. Screening the initial clinical indicators using the multiple comparison method to determine the second clinical indicator:

[0086] The multiple comparison method was used to retain the samples with p-value < 0.05 in the statistical test results of the above 16 clinical indicators. The p-value was obtained by performing multiple comparisons on the clinical indicators among multiple experimental groups, firstly testing their homogeneity by Bartlett Test (corresponding to the R language program function bartlett.test) (taking p-value < 0.05 as a non-null hypothesis), and then calculating their p-values ​​by the oneway.test function of the R language program package. If p < 0.05, it means that the clinical indicator is significant.

[0087] c. Use the PerMANOVA multivariate analysis of variance method to screen the second clinical indicator and determine the significant clinical indicator.

[0088] The permaonva analysis method was used to further screen out clinical indicator information that had a significant relationship with the expression of the flora profile of human fecal samples (i.e., the second clinical indicator);

[0089] d. The significant clinical index information obtained in step c is used as the response variable to perform a final correction on the second differential flora sample set obtained in step S3 above, and the differential flora whose p-value after regression is still less than 0.05 is screened out to obtain the differential flora (third differential flora sample set) after eliminating the interference of clinical factors, specifically including:

[0090] The clinically significant index information obtained in step c is used as the response variable, and the second differential substance bacterial cluster obtained in step S3 is used as the independent variable. A logistic regression model is established to obtain the p-value. If the p-value of the differentially expressed bacterial cluster is less than 0.05, it means that these bacterial clusters are not affected by the clinical indicators, that is, they are differential bacterial clusters after excluding the interference of clinical factors.

[0091] e. The differential flora obtained by the above four comparison strategies of prHS and prMS, prHS and prLS, prMS and prLS, and prHS+prMS and prLS after excluding the interference of clinical factors was merged, which was the third differential flora sample set. Three different machine learning algorithms, namely random forest method, support vector machine method and LASSO, were used for further screening and analysis. Based on the key candidate differential flora combinations finally screened out by these three different algorithms, a joint ROC analysis was carried out, and finally the final differential flora combination panel was determined according to the obtained AUC value, specifically:

[0092] First, the third differential flora sample set was divided into a training set and a test set (the ratio of training set to test set was 8:2), and different algorithms were used to screen differential flora, as follows:

[0093] Random forest method: Use the R package randomForest to fit the random forest model to the training set, and finally determine the treeNum in the fitting model through parameter tuning. Fit the training set with the optimal parameters, and test the test set to obtain the confusion matrix of the prediction results. Further, use the rfcv function in the R package randomForest to perform 10-fold cross validation to obtain the relationship between variable selection and the average error rate of model classification, and obtain the optimal differential flora based on MeanDecreaseAccuracy. The larger the value, the greater the importance of the variable.

[0094] Support vector machine (SVM): Support vector machine is a two-class classification model. Its basic model is a linear classifier that finds the separating hyperplane with the maximum margin in the feature space. SVM is an algorithm for the post-selection of a sequence based on the maximum margin principle. In the first iteration, the SVM model is optimized for all feature sets in the data set. Then the score of each feature is calculated and sorted in descending order. The feature set with the smallest score is recorded, and then the feature with the smallest score is deleted. It is iterated again until only one feature is left. The training set is trained using the svm function of the R language to obtain the relationship between variable selection and the average error rate of model classification, and the feature set with the lowest error rate is selected.

[0095] LASSO: LASSO imposes regression penalties on all variables, making the coefficients of relatively unimportant variables become 0, thus being excluded from modeling, and then selecting independent variables that have a greater impact on the dependent variable and calculating the corresponding regression coefficients, and finally obtaining a prediction model. The model is trained on the training set using the R language glmnet. The lambda value with the smallest mean square error is selected through the mean square error under different parameters lambda. A vertical line is drawn through the position of the corresponding penalty value of the change trajectory diagram of each independent variable coefficient. Each curve represents a variable, and the variable that intersects with the penalty value is the variable finally included in the model. The ordinate corresponding to the variable is the regression coefficient of the variable, and the regression coefficient is also the contribution of the variable.

[0096] Secondly, the differential bacterial communities were screened out through random forest, support vector machine and LASSO. The data of these differential bacterial communities were found in the normalized data. The differentially expressed bacterial communities were used as the response variables, and the groups were divided into 1 as the experimental group and 0 as the control group as the dependent variables to establish a logistic regression model.

[0097] Finally, the probability given by logistic regression was calculated to draw the joint ROC curve. The larger the AUC value, the better the classification effect of the model. The optimal differential flora combination panel was determined based on the size of the AUC.

[0098] Step S3: Using the optimal differential flora combination panel and combining the k-fold cross-validation method, the logistic regression model, the random forest model, the support vector machine and the LASSO model are trained respectively to obtain a trained logistic regression model, a trained random forest model, a trained support vector machine and a trained LASSO model.

[0099] First, the best differential flora combination panels were determined for the three different comparison strategies of prHS and prMS, prHS and prLS, and prMS and prLS. Then, the best differential flora combination panels were merged and randomly sampled into training sets and test sets, with a ratio of 7:3 for training sets: test sets.

[0100] Secondly, for the training sets of different comparison strategies, the caret package was used to perform 10-fold cross-validation for the logistic regression, LASSO, support vector machine, and random forest models, respectively, with 1000 repetitions. The cross-validation was mainly performed using trainControl in the caret package in the R language.

[0101] The steps involved in the 10-fold cross validation are:

[0102] (1) Randomly divide the dataset into 10 subsets, (2) For each selected subset of data points, use that subset as a validation set; use all remaining subsets for training purposes; train the model and evaluate it on the validation set or test set; calculate the prediction error, (3) repeat the above steps 10 times, (4) generate the overall prediction error by taking the average of the prediction errors.

[0103] Step S4: Using ROC analysis, according to the AUC value, use the train function to train the logistic regression, LASSO, support vector machine, and random forest models respectively, calculate the prediction probability of the test set under the trained model, and draw the ROC curves of different models at the same time. The larger the AUC value, the better the model effect, and determine the optimal model. Finally, merge the microbial communities obtained by the logistic regression, LASSO, support vector machine, and random forest models to obtain microbial biomarkers for screening and detecting tumor radiotherapy sensitivity.

[0104] Finally, the following 12 microbial combinations were obtained: s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__bacterium_New, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, The panel composed of s__Anaerostipes_unclassified and s__Prevotella_stercorea had the largest AUC (area under the curve) value. The AUC value of the 12 differential bacterial communities in prHS+prMSvsprLS reached 0.913, the AUC value in prHSvsprLS reached 0.890, the AUC value in prHSvsprMS reached 0.552, and the AUC value in prMSvsprLS reached 0.798, revealing that the 12 intestinal flora have potential predictive value for the radiotherapy sensitivity of patients with colorectal cancer, and it is expected to be used as a fixed marker for prediction by collecting patient feces and performing 16srRNA high-throughput sequencing analysis.

[0105] Example 2

[0106] This example detects the sensitivity of tumor radiotherapy by using the microbial markers screened in Example 1. The critical value of the logistic regression model constructed by using the microbial markers described in Example 1 (s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, s__Anaerostipes_unclassified, s__Prevotella_stercorea) predicts the radiotherapy sensitivity of colorectal cancer patients.

[0107] Among them, the reagents are divided into stool samples to be tested, stool samples of colorectal cancer patients who are insensitive to radiotherapy, and stool samples of colorectal cancer patients who are moderately sensitive to radiotherapy.

[0108] Determination method: The stool samples to be tested vs. the stool samples of colorectal cancer patients who are insensitive to radiotherapy are used to predict the optimal threshold of P using the logistic regression model constructed by Metaboanalyst6.0 (https: / / www.metaboanalyst.ca / ). When P < 0.62, it indicates that the tumor patient is insensitive to radiotherapy, and when P ≥ 0.62, it indicates that the tumor patient is moderately sensitive to radiotherapy or highly sensitive to radiotherapy.

[0109] In order to further determine whether the tumor patients are moderately sensitive or highly sensitive to radiotherapy, a logistic regression model was constructed by comparing the stool samples with P ≥ 0.62 with the stool samples of colorectal cancer patients with moderate sensitivity to radiotherapy to predict the optimal threshold of P. When P < 0.48, it indicates that the tumor patients are moderately sensitive to radiotherapy, and when P ≥ 0.48, it indicates that the tumor patients are highly sensitive to radiotherapy.

[0110] The inclusion criteria for the study subjects (tumor patients) included: (1) patients who received surgical treatment after neoadjuvant radiotherapy or chemoradiotherapy; (2) patients whose radiotherapy regimen was long-term radiotherapy. The exclusion criteria included: (1) patients with any other type of malignant tumor or severe immune, nervous, digestive, or blood system diseases; (2) patients whose plasma samples could not be obtained; (3) patients with colorectal adenocarcinoma that had metastasized; and (4) patients whose radiotherapy regimen was short-term radiotherapy.

[0111] In this embodiment, there are 22 stool samples to be tested, 31 stool samples from patients with colorectal cancer who are insensitive to radiotherapy, and 60 stool samples from patients with colorectal cancer who are moderately sensitive to radiotherapy. Among them, patients with colorectal cancer who are insensitive to radiotherapy refer to patients with NAR scores > 16, and patients with colorectal cancer who are moderately sensitive to radiotherapy refer to patients with NAR scores of 8 to 16. The NAR score is significantly correlated with overall survival (OS).

[0112] The logistic regression model of the stool samples to be tested vs the stool samples of colorectal cancer patients who are not sensitive to radiotherapy is logit(P)=log(P / (1-P))=0.004+0.188

[0113] s__Roseburia_inulinivorans+81.813s__Prevotella_nigrescens-9.098s__Enterobacter_cloacae-9.461s__Bacteroides_clarus-0.753s__Anaerostipes_unclassified-0.492s__Cl ostridioides_difficile-63.111s__Campylobacter_gracilis-0.119s__Prevotella_stercorea-0.537s__Mogibacterium_diversum-0.095s__Schaalia_odontolytica-0.46s__Alistipes_obesi. The optimal threshold (or cutoff value) for predicting P is 0.62. When P < 0.62, it indicates that the tumor patient is low sensitive to radiotherapy. When P ≥ 0.62, it indicates that the tumor patient is highly sensitive to radiotherapy.

[0114] In order to further determine whether the tumor patients are moderately sensitive or highly sensitive to radiotherapy, the stool samples with P ≥ 0.62 were compared with the stool samples of colorectal cancer patients with moderate sensitivity to radiotherapy, and the logistic regression model constructed using the microbial markers screened in Example 1: s__Roseburia_inulinivorans, s__Prevotella_stercorea, s__Anaerostipes_unclassified, s__Clostridioides_difficile, s__Prevotella_nigrescens, s__Schaalia_odontolytica and s__Alistipes_obesi was used to predict the optimal threshold of P.

[0115] The logistic regression model of the stool samples to be tested with P≥0.62 vs. the stool samples of colorectal cancer patients with moderate sensitivity to radiotherapy was logit(P)=log(P / (1-P))=-0.043+0.581s__Roseburia_inulinivorans+0.016s__Prevotella_stercorea+0.997s__Anaerostipes_unclassified+0.009s__Clostridioides_difficile-0.946s__Prevotella_nigrescens+0.283s__Schaalia_odontolytica-0.765s__Alistipes_obesi. The optimal threshold (or cutoff value) for predicting P was 0.48. When P<0.48, it indicated that the tumor patient was moderately sensitive to radiotherapy. When P≥0.48, it indicated that the tumor patient was highly sensitive to radiotherapy. Colorectal cancer patients with high radiosensitivity are those with a NAR score of less than 8.

[0116] According to the method of logistic regression model fitting, the ROC curve is drawn. Figure 5 The results showed that the AUC of the stool samples to be tested vs the stool samples of colorectal cancer patients who were insensitive to radiotherapy reached 0.913, and the AUC value of the stool samples to be tested with P ≥ 0.62 vs the stool samples of colorectal cancer patients who were moderately sensitive to radiotherapy reached 0.552.

[0117] The ROC curve is drawn according to the bacterial flora involved in the logistic regression model equation. Figure 6 The results showed that the AUC of the fecal samples to be tested using the microbiota markers for diagnosis of colorectal cancer patients who were not sensitive to radiotherapy reached 0.907, and the AUC value of the fecal samples to be tested with P ≥ 0.62 vs. the fecal samples of colorectal cancer patients who were moderately sensitive to radiotherapy reached 0.571.

[0118] The relative levels of the flora of the colorectal cancer patient group that was insensitive to radiotherapy in Example 1 were brought into the prHS+prMSvsprLS logistic regression model to verify the grouping, and the P values ​​of each sample were 0.482658179, 0.499993271, 0.500693147, 0.492165009, 0.500693147, 0.421449718, 0.411294497, 0.476149864, 0.154763929, and 0.478 716206, 0.486857377, 0.414507253, 0.488936299, 0.008361682, 6.79526E-05, 0.495352268, 0.500693147, 0.500693147, 0.487279704, 0.443473258, 0.000487226, and 0.495477066 all met the P < 0.62. The verification results also proved that tumor patients have low sensitivity to radiotherapy.

[0119] It can be seen that the microbiota markers of the present invention have high accuracy in detecting tumor radiotherapy sensitivity.

[0120] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A microbial marker for detecting tumor radiotherapy sensitivity, characterized in that: The bacterial flora markers include one or more of s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__bacterium_New, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, s__Anaerostipes_unclassified, and s__Prevotella_stercorea.

2. Use of the microbial marker according to claim 1 in the preparation of a product for diagnosing tumor radiotherapy sensitivity.

3. Use of the microbial marker according to claim 1 in the preparation of a product for diagnosing tumor radiotherapy sensitivity grading.

4. The use according to claim 3, characterized in that: The tumor radiotherapy sensitivity classification is divided into tumor radiotherapy high sensitivity and tumor radiotherapy moderate sensitivity.

5. A system for predicting tumor radiotherapy sensitivity using the microbial markers according to claim 1, characterized in that: It includes a nucleic acid sample separation unit, which is used to separate a fecal flora nucleic acid sample from a test sample; A data processing unit, using the microbial markers of claim 1 to establish a logistic regression model to obtain a critical value; The result determination unit is used to compare the critical value obtained by the data processing unit with the set diagnosis value.

6. The system according to claim 5, characterized in that The logistic regression model is a first logistic regression model and a second logistic regression model; The first logistic regression model is a logistic regression model constructed for the stool sample to be tested vs. the stool sample of the colorectal cancer patient who is not sensitive to radiotherapy. When the critical value is <0.62, it indicates that the tumor patient is not sensitive to radiotherapy. When the critical value is ≥0.62, it indicates that the tumor patient is moderately sensitive to radiotherapy or highly sensitive to radiotherapy. The second logistic regression model is a logistic regression model constructed by comparing the stool samples to be tested with a critical value ≥ 0.62 with the stool samples of colorectal cancer patients with moderate sensitivity to radiotherapy. When the critical value is < 0.48, it indicates that the tumor patient is moderately sensitive to radiotherapy, and when the critical value is ≥ 0.48, it indicates that the tumor patient is highly sensitive to radiotherapy.

7. The system according to claim 6, characterized in that When the first logistic regression model is established using the microbial markers described in claim 1, the microbial markers are s__Schaalia_odontolytica, s__Roseburia_inulinivorans, s__Mogibacterium_diversum, s__Campylobacter_gracilis, s__Alistipes_obesi, s__Prevotella_nigrescens, s__Clostridioides_difficile, s__Bacteroides_clarus, s__Enterobacter_cloacae, s__Anaerostipes_unclassified, and s__Prevotella_stercorea.

8. The system according to claim 6, characterized in that When the second logistic regression model is established using the microbial markers described in claim 1, the microbial markers are s__Roseburia_inulinivorans, s__Prevotella_stercorea, s__Anaerostipes_unclassified, s__Clostridioides_difficile, s__Prevotella_nigrescens, s__Schaalia_odontolytica and s__Alistipes_obesi.

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