Intestinal microbial marker for predicting curative effect of drug on inflammatory bowel disease and application of intestinal microbial marker

Abstract

The invention provides application of intestinal flora as a marker in preparation of a product for evaluating the treatment effect of inflammatory bowel diseases. The feasibility of predicting the curative effect of the medicine for treating the inflammatory bowel disease patient by using the microbial spectrum can accurately discriminate the patient needing to optimize the treatment scheme in the early stage of the disease, so that the prognosis condition of the patient is remarkably improved. Meanwhile, the invention provides a construction method of an intestinal flora biomarker prediction model, accurate prediction of the ineffective condition of drug treatment is realized through the combination of faecobacteria prausnitzii, Blauratia massiensis and coma faecalis, and the potential of the intestinal flora biomarker prediction model as a new tool for early recognition of a patient possibly needing optimized treatment is highlighted.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and particularly relates to intestinal microbial markers for predicting the efficacy of drugs (such as 5-aminosalicylic acid, anti-tumor necrosis factor, ustekinumab) on inflammatory bowel disease and their applications. Background Art

[0002] Inflammatory Bowel Disease (IBD) includes Ulcerative Colitis (UC) and Crohn's disease (CD), which are chronic gastrointestinal diseases characterized by repeated inflammation, various complications and unknown etiologies. Ulcerative colitis occurs in the colorectum, with persistent or recurrent diarrhea, mucopurulent bloody stools, and complications such as toxic megacolon, massive gastrointestinal bleeding, and canceration. Crohn's disease can involve the entire digestive tract including the colon, small intestine, stomach, and esophagus. Patients often have abdominal pain and diarrhea symptoms, are prone to malnutrition, and can have complications such as intestinal stenosis, intestinal fistula, and intestinal obstruction, seriously affecting the quality of life.

[0003] Current treatment drugs mainly include 5-aminosalicylic acid (5-ASA), glucocorticoids, immunosuppressants, and biological agents. Mucosal healing (MH) is the current treatment goal for IBD because it is associated with better disease prognosis.

[0004] 5-ASA is the first-line treatment drug for UC, with the least side effects among all IBD treatment drugs and a low price. However, less than 50% of patients can achieve MH after 5-ASA treatment. Identifying patients who fail 5-ASA treatment at an early stage of the disease can enable earlier up-titration of treatment, so that patients can reach the treatment goal as soon as possible and improve long-term prognosis. However, the current clinically applied detection indicators are not sufficient to predict the treatment outcome of 5-ASA. Therefore, there is an urgent need for indicators for predicting treatment efficacy, especially non-invasive detection methods.

[0005] Anti-tumor necrosis factor (tumor necrosis factor-α, TNF-α) preparations are the earliest and most widely used biological agents, which can induce and maintain disease remission and MH. However, 20% - 40% of IBD patients have primary non-response to anti-TNF-α preparations, and another about 50% of patients have secondary loss of response. Interleukin (IL)-12 / 23 inhibitor, ustekinumab, is also a biological agent, which was approved for the treatment of inflammatory bowel disease in China in 2020. Recent studies have suggested that intestinal microbiota has certain potential in predicting the efficacy of biological agents in IBD patients, but reliable microbial markers are still to be discovered.

[0006] The gut microbiome is at the core of the pathogenesis of IBD. Existing studies have found that it can interfere with pharmacokinetics and pharmacodynamics, affecting the absorption, distribution, metabolism, excretion processes of drugs in the body and their ultimate efficacy. Therefore, it is of great practical significance to conduct in-depth research to explore microbial markers that can predict the efficacy of therapeutic drugs in IBD. Summary of the Invention

[0007] In view of the lack of prediction of the treatment effect of inflammatory bowel disease in the prior art, the present invention provides the use of gut microbiota as markers in the preparation of products for predicting the efficacy of drugs against inflammatory bowel disease. The gut microbiota markers of the present invention have high sensitivity and good specificity, and have broad application prospects for estimating the efficacy of drugs against inflammatory bowel disease.

[0008] In a first aspect of the present invention, there is provided the use of a gut microbiota marker in the preparation of a product for predicting the efficacy of a drug against inflammatory bowel disease.

[0009] Preferably, the gut microbiota marker is selected from one or more of Faecalibacterium prausnitzii, Blautia massiliensis, and Phascolarctobacterium faecium, especially a combination of the three.

[0010] Preferably, the inflammatory bowel disease is selected from one or both of ulcerative colitis and Crohn's disease.

[0011] Preferably, the drugs for treating inflammatory bowel disease are selected from one or more of 5-aminosalicylic acid, glucocorticoids, immunosuppressants, and biological agents.

[0012] More preferably, the drugs for treating inflammatory bowel disease are selected from one or more of anti-tumor necrosis factor, interleukin (IL)-12 / 23 inhibitors, and 5-aminosalicylic acid.

[0013] In some embodiments of the present invention, the inflammatory bowel disease is ulcerative colitis and the drug is 5-aminosalicylic acid.

[0014] In some embodiments of the present invention, the inflammatory bowel disease is ulcerative colitis and the drug is anti-tumor necrosis factor.

[0015] In some embodiments of the present invention, the inflammatory bowel disease is ulcerative colitis or Crohn's disease and the drug is an interleukin-12 / 23 inhibitor (such as ustekinumab).

[0016] The prediction criterion for the efficacy of a drug on inflammatory bowel disease is as follows: detect the abundance of intestinal microbial markers in the fecal samples of patients. Compared with the normal reference, the lower the abundance of intestinal microbial markers in the patient, it can be predicted that the drug is ineffective in treating the patient's inflammatory bowel disease, and it is necessary to consider replacing other biological agents and adjusting the treatment plan.

[0017] Preferably, the product can be a reagent, a kit, a test strip or an instrument platform.

[0018] Specifically, the test sample of the product is feces.

[0019] In the second aspect of the present invention, a kit for predicting the efficacy of a drug on inflammatory bowel disease is provided, which includes a product for detecting the intestinal microbial markers described in the first aspect (for example, a reagent for detecting the 16S rRNA of the intestinal microbial markers), the drug and the inflammatory bowel disease as described in the first aspect of the present invention.

[0020] Specifically, the kit further contains a reagent for extracting intestinal microbial genomic DNA from the sample.

[0021] In the third aspect of the present invention, a method for screening intestinal flora biomarkers is provided, including the following steps:

[0022] S1. Obtain the clinical information data and fecal samples of the disease during the baseline and follow-up periods, and conduct analysis;

[0023] S2. Extract DNA from the fecal samples and perform metagenomic sequencing screening;

[0024] S3. Preprocess the DNA sequence data of the disease during the baseline and follow-up periods and perform microbiome analysis to determine the intestinal flora biomarkers.

[0025] Preferably, the disease is inflammatory bowel disease, including one or both of ulcerative colitis and Crohn's disease.

[0026] Preferably, the DNA extraction method includes using the Omega MAG-BIND Soil DNA Kit (M5635-02) to extract intestinal microbial genomic DNA, using a Qubit 4 fluorometer to quantify the DNA, and evaluating the quality by agarose gel electrophoresis.

[0027] Preferably, the DNA sequence data and microbiome processing software include one or more of Trimmomatic (v0.39), KNEADDATA (v0.12.0), Metaphlan (v4.0.6), HUMAnN (v3.7), Chocophlan, Uniref90 database, R (v4.2.0), and Huttenhower Lab Galaxy.

[0028] In a fourth aspect of the present invention, there is provided a prediction model for the efficacy of a drug on inflammatory bowel disease, wherein the prediction model uses the intestinal microbial markers described in the first aspect as variables.

[0029] In an embodiment of the present invention, the variables of the prediction model are a combination of F. prausnitzii, B. massiliensis, and P. faecium.

[0030] Preferably, the prediction model is a random forest model.

[0031] In a fifth aspect of the present invention, there is provided a method for constructing a prediction model as described in the fourth aspect, comprising the following steps: inputting the marker data in the sample into a random forest (RF) binary classification machine learning model, and using the relative abundance table to train the model.

[0032] In a sixth aspect of the present invention, there is provided a system for predicting the efficacy of a drug on inflammatory bowel disease, which includes:

[0033] A data processing module for receiving or inputting the intestinal microbial abundance data in the fecal samples of patients with inflammatory bowel disease, wherein the intestinal microbes include the intestinal microbial markers described in the first aspect of the present invention;

[0034] A judgment and output module for obtaining and outputting a prediction result on whether the drug treatment for the patient with inflammatory bowel disease is effective through the prediction model described in the fourth aspect of the present invention after the reception or input is completed.

[0035] In some embodiments of the present invention, the system further includes a data processing module for collecting the intestinal microbial abundance data in the sample.

[0036] In a seventh aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, which when executed by a processor, can implement the functions of the system described in the sixth aspect of the present invention.

[0037] In an eighth aspect of the present invention, there is provided an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the functions of the system described in the sixth aspect of the present invention.

[0038] In the ninth aspect of the present invention, there is provided a method for predicting the efficacy of a drug on inflammatory bowel disease, which comprises the following steps:

[0039] (1) Detecting the abundance of gut microbiota in the fecal sample of the patient to be predicted, wherein the gut microbiota includes the gut microbiota markers described in the first aspect of the present invention;

[0040] (2) Inputting the data obtained in step (1) into the prediction model described in the fourth aspect of the present invention, and outputting the result of whether the drug treatment for the patient is effective.

[0041] Specifically, the method further comprises the following steps: extracting DNA and performing metagenomic sequencing on the fecal sample of the patient to be predicted.

[0042] Specifically, the patient is a mammal, such as a human.

[0043] Advantages of the present invention:

[0044] 1. The present invention provides the application of gut microbiota as a marker in the preparation of products for predicting the efficacy of drugs on inflammatory bowel disease, and the feasibility of using the microbial profile to predict the efficacy of drug treatment for patients with inflammatory bowel disease. This achievement can accurately identify patients who need to upgrade the treatment plan at an early stage of the disease, thereby significantly improving the prognosis of the patients.

[0045] 2. The present invention provides a method for constructing a gut microbiota prediction model, which can accurately predict the ineffective situation of drug treatment through the combination of F. prausnitzii, B. massiliensis and P. faecium, highlighting its potential as a new tool for early identification of patients who may need treatment upgrade. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings, wherein:

[0047] Figure 1 : Main differences in gut microbiota between the effective and ineffective groups at baseline. A: Stacked bar charts describe the phylum-level differences in gut microbiota composition between the two groups; B: Two groups of Proteobacteria; C: α-diversity of the microbiota; D: β-diversity analysis at the species level using Aitchison distance (PCoA plot); E: Baseline differences in species between the two groups; F: β-diversity analysis of the functional profile using Aitchison distance (PCoA plot). G: Baseline differences in pathways between the two groups; H: Spearman correlation between species and functions.

[0048] Figure 2: Longitudinal changes in species and pathways between the effective and ineffective groups. A: There was a significant difference in species richness between the two groups at follow-up; B: The relative abundance of the three bacterial species continued to decrease in ineffective patients at baseline and follow-up; C: The log2-fold change (FC) of Faecalibacterium prausnitzii after treatment compared with the baseline sample.

[0049] Figure 3 : ROC curves of the test cohort for the RF classifier constructed based on microbial variables at baseline.

[0050] Figure 4 : ROC curve of IBDMDB cohort based on RF classifier.

[0051] Figure 5 : The relative abundance of Faecalibacterium prausnitzii, Blautia massiliensis and Pseudomonas aeruginosa in IBDMDB.

[0052] Figure 6 : ROC curve of the RF classifier constructed by intestinal signature flora to predict the efficacy of anti-tumor necrosis factor therapy in patients with ulcerative colitis.

[0053] Figure 7 : ROC curve of the RF classifier constructed by intestinal signature flora to predict the efficacy of ustekinumab treatment in patients with inflammatory bowel disease. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0055] The term "patient" encompasses mammals. Examples of mammals include, but are not limited to, any member of the class Mammalia: humans, non-human primates (such as chimpanzees and other apes and monkey species); farm animals, such as cattle, horses, sheep, goats, pigs; domestic animals, such as rabbits, dogs and cats; experimental animals, including rodents, such as rats, mice and guinea pigs, etc. In one aspect, the mammal is a human.

[0056] The term "microorganism" in the present invention refers to a large class of biological groups including bacteria, viruses, fungi, some small protozoa, microscopic algae, etc.

[0057] Statistical analysis: The Kolmogorov-Smirnov one-sample test was used to test the normal distribution of the data. Continuous variables were expressed as mean ± standard deviation, and categorical variables were expressed as frequency or percentage. Independent sample t-tests were used for between-group comparisons of normally distributed data, while Mann-Whitney-Wilcoxon tests were used for non-normally distributed data. Fisher's exact test or χ 2 test was used for categorical variables. Spearman correlation was used to explore the correlations between continuous variables. All statistical tests were two-tailed, with a significance level of 0.05.

[0058] Ethical considerations: The case study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (2017-P2-094-01), and informed consent was obtained from all participants.

[0059] Patient sample collection and preliminary analysis in Example 1

[0060] 1. Sample collection

[0061] In the prospective IBD registry at Beijing Friendship Hospital, all patients involved were diagnosed with UC according to the Lennard-Jones criteria (Lennard-Jones, J.E., Classification of inflammatory bowel disease. Scand J Gastroenterol Suppl 1989, 170, 2-6; discussion 16-9). Follow-up assessments were conducted every 3 months. Detailed clinical information, including demographic details, history of inflammatory bowel disease, clinical manifestations, medication profiles, laboratory test results, and endoscopic findings, was collected at the time of enrollment and at each visit.

[0062] Patients were included according to the following criteria: (1) aged 18 years or older; (2) diagnosed with active UC at onset (Mayo endoscopic score [MES] ≥ 2); (3) receiving standardized 5-ASA treatment; (4) having good compliance with 5-ASA treatment; (5) undergoing colonoscopy 6-12 months after 5-ASA treatment to evaluate UC.

[0063] Exclusion criteria were as follows: (1) those with a history of tumor or gastrointestinal surgery; (2) those who had taken antibiotics within 3 months before fecal specimen collection; (3) those who were microbiologically confirmed to have infectious colitis within 30 days before fecal specimen collection; (4) those who had been exposed to corticosteroids, immunosuppressants, or received biotherapy.

[0064] The standardized 5-ASA treatment regimen is defined as a sufficient dose of 5-ASA for induction of remission (oral 5-ASA ≥ 3 g / day and / or topical treatment ≥ 1 g / day) or maintenance (oral 5-ASA ≥ 2 g / day and / or topical treatment 2 - 3 g / week).

[0065] Adherence to 5-ASA treatment: Good medication adherence was determined according to the 8-item Morisky Medication Adherence Scale (MMAS) score of 6 - 8. Mucosal healing (MH) (MES = 0 or 1) achieved after 6 - 12 months of treatment was considered effective 5-ASA treatment. Patients who could not achieve MH were assigned to the ineffective group. (For UC patients, MH was defined as the absence of tissue friability, bleeding, erosion, and ulcers in all visible areas of the intestinal lumen mucosa.)

[0066] All fecal samples self-collected by patients at home were collected using the fecal collection tubes provided by the researchers in advance and transported to the laboratory in a timely manner within 24 hours. The fecal samples were stored at -80 °C before DNA extraction and metagenomic sequencing.

[0067] 2. Preliminary analysis

[0068] A total of 51 eligible UC patients participated in the study. Among these patients, 26 achieved MH during the follow-up. A total of 75 fecal samples were collected, including 51 at baseline and 24 after treatment (14 in the effective group and 10 in the ineffective group). The specific results of the baseline characteristics of the included patients are shown in Table 1. The results showed that there were no significant differences between the 5-ASA effective group and the ineffective group in terms of age, gender, disease severity, inflammation severity, or inflammatory markers. This means that clinical characteristics cannot be used as predictive indicators for 5-ASA.

[0069] Table 1. Baseline characteristics of UC patients included in this study

[0070]

[0071]

[0072] Example 2 Screening markers

[0073] 1. Fecal DNA extraction and Shotgun metagenomic sequencing

[0074] For the fecal samples collected in Example 1, the intestinal microbial genomic DNA was extracted using the OMEGA Mag-Bind Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA). The extracted samples were stored at -20 °C for further evaluation. The DNA was quantified using a Qubit 4 fluorometer (Invitrogen, USA), and the quality was evaluated by agarose gel electrophoresis. DNA libraries were prepared using the Illumina TruSeq Nano DNA LT Library Preparation Kit (400 bp insert size) (Illumina, USA) and sequenced on the Illumina Novaseq platform, generating 6 GB of data per sample with 2×150 bp paired-end reads.

[0075] 2. Preprocessing of sequence data and microbiome analysis

[0076] The raw sequencing data was processed using Trimmomatic (v0.39) to remove adapter sequences, perform quality control, and filter low-quality reads. Subsequently, KneadData (v0.12.0) was used to remove reads aligned to the human reference genome to ensure that the subsequent analysis only included sequences of microbial origin. Fecal microbial taxonomic analysis was performed using MetaPhlAn (v4.0.6) for species annotation, and the microbial abundances at each taxonomic level (phylum, class, order, family, genus, species) were calculated.

[0077] HUMAnN (v3.7) was used to quantitatively analyze the relative abundances of functional pathways in each fecal sample using DIAMOND in combination with the ChocoPhlAn and EC-filtered UniRef90 databases. Only species and functional features that were present in at least 10% of the patient samples were retained. Subsequently, the raw read counts were converted to relative abundances by normalizing the total reads of each sample to ensure data comparability.

[0078] Data analysis of the fecal sample microbiome was performed using R (v4.2.0). Alpha diversity analysis was evaluated using the Shannon index and species richness index. Beta diversity, which visualizes the community structure and functional changes between different samples, was visualized by principal coordinates analysis (PCoA) based on Aitchison distance. Permutational multivariate analysis of variance (Permanova, n = 999) was used to evaluate the effect of confounding factors on Aitchison distance.

[0079] Multivariate analysis was performed using the linear model (Maaslin2) (v1.18.0) statistical framework implemented in the Huttenhower Lab Galaxy instance (http: / / huttenhower.sph.harvard.edu / galaxy / ) to identify differential microbiome and functional features, with the significance set at p < 0.05.

[0080] 3. Results

[0081] a) The situation of Proteobacteria in the fecal samples of patients in the effective and ineffective groups after 5-ASA treatment at the baseline period

[0082] The situation of Proteobacteria in the fecal samples of patients in the effective and ineffective groups after 5-ASA treatment at the baseline period was analyzed, and the specific results are as Figure 1 shown. In the samples of patients in the ineffective 5-ASA treatment group at the baseline period, an increased abundance of Proteobacteria was shown (p = 0.035) ( Figure 1 A, B). No significant difference in α-diversity was observed between the two groups in the overall composition of the gut microbiome ( Figure 1 C). A tendency of separation was observed in the β-diversity analysis (Aitchison distance), and the difference was statistically significant (p = 0.054) ( Figure 1 D).

[0083] Through multivariate analysis with the linear model (Maaslin2), a species difference map between the two groups was identified ( Figure 1 E). In the baseline fecal samples of patients in the ineffective 5-ASA treatment group, Faecalibacterium prausnitzii, Blautia massiliensis, Phascolarctobacterium faecium, Blautia SGB4815, Coprococcus comes, and Peptostreptococcus stomatis were significantly reduced, while Klebsiella pneumoniae, Eggerthella sinensis, and GGB80090 SGB1690 were significantly increased.

[0084] For the functional potential of the gut microbial community, there was no significant difference in β-diversity ( Figure 1 F). Pathways crucial for the synthesis of short-chain fatty acids (SCFAs), such as the superpathway of pyruvate fermentation to butyrate and acidogenic fermentation by Clostridium acetobutylicum, were significantly enriched in the baseline fecal samples of patients in the effective group ( Figure 1G). Notably, baseline gondoic acid biosynthesis was negatively correlated with several species enriched in the effective group, including F. prausnitzii, C. comes, Blautia SGB4815, and P. stomatis( Figure 1 H).

[0085] b) The situation of Proteobacteria in the fecal samples of patients in the effective and ineffective groups after 5-ASA treatment

[0086] Analyze the situation of Proteobacteria in the fecal samples of patients in the effective and ineffective groups after 5-ASA treatment. The specific results are as Figure 2 shown. After 5-ASA treatment, 12 bacteria, including F. prausnitzii, B. massiliensi, P. faecium, Ruminococcus sp AF 13_28, Blautia sp Marseille P3087, Anaerostipes hadrus, Lacrimispora celerecrescens, Bifidobacterium longum, Actinomyces massiliensis, Enterococcus SGB6173, Actinomyces_SGB17163, and Lachnospiraceae_bacterium, were reduced in the ineffective group; while 6 bacteria, including Escherichia coli, Enterococcus avium, Enterococcus faecalis, GGB3746 SGB5089, Limosilactobacillus mucosae, and Lacticaseibacillus paracasei, were enriched in the ineffective group( Figure 2 A).

[0087] In the baseline and follow-up of patients in the 5-ASA ineffective group, three bacteria, F. prausnitzii, B. massiliensis, and P. faecium, continuously decreased in fecal samples( Figure 2 B). In patients in the 5-ASA ineffective group, the abundance of F. prausnitzii after treatment was even lower than the baseline level( Figure 2 C).

[0088] Example 3 Construction of a 5-ASA efficacy prediction model

[0089] 1. Construction of a Random Forest Diagnostic Model

[0090] Using the Random Forest (RF) binary classifier machine learning model in Scikit-Learn (v.1.5.2) software, this algorithm has shown superior performance compared to other machine learning models used for microbiota data in previous studies. The relative abundance table was used to train the model. Cross-validation was performed by iteratively (10 times) training the RF model, and the division ratio of the training set / test set was 70%-30%. The predict_proba function was used to estimate the probability of each sample for different classes. The Youden index method was used to determine the optimal threshold on the training set. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) were used to evaluate the model performance. The model prediction performance was measured by multiple metrics such as sensitivity, specificity, accuracy, Positive Predictive Value (PPV), and Negative Predictive Value (NPV).

[0091] Considering that at baseline and after treatment, given that the abundances of F. prausnitzii, B. massiliensis, and P. faecium in the non-responder group continuously decreased at baseline and after treatment, the low abundances of these three species at baseline were used to construct a prediction model for 5-ASA treatment failure. A robust random forest classification model was constructed, and the average AUC of this model for predicting 5-ASA treatment non-response was 0.80( Figure 3 ).

[0092] Example 4 Validation of the 5-ASA Efficacy Prediction Model

[0093] To further test the general applicability of the model, the results of the prediction model were externally validated in the Inflammatory Bowel Disease Multi-Omics Database (IBDMDB)( https: / / ibdmdb.org / results ). Eligible patients from the IBDMDB cohort (n = 15) were used as an independent dataset (Table 2), and the prediction model was externally validated. The AUC of this model for predicting 5-ASA treatment failure was 0.82, and the specificity, NPV, and PPV were 0.88, 0.70, and 0.80 respectively( Figure 4 ). Figure 5 F. prausnitzii, B. massiliensis, and P. faecium were identified as specific bacterial markers, and these markers continuously decreased in non-responder patients at baseline and during follow-up. Therefore, the combination of the three strains of F. prausnitzii, B. massiliensis, and P. faecium as gut microbiota markers had the best prediction effect.

[0094] Table 2. Baseline Characteristics of the Validation Cohort

[0095]

[0096]

[0097] Example 5: Verification of the Prediction Model for the Therapeutic Effects of Other Biological Agents

[0098] The publicly available dataset PRJNA685168 was selected to validate the constructed model for predicting the efficacy of biological agents in treating patients with inflammatory bowel disease. This dataset can be obtained from the PRISM metagenomics data in the Sequence Read Archive (SRA) (https: / / www.ncbi.nlm.nih.gov / Traces / study / ?acc=PRJNA685168&o=acc_s%3Aa). The study subjects in this dataset included patients with inflammatory bowel disease (10 cases) and ulcerative colitis (11 cases). These patients received anti-tumor necrosis factor and anti-interleukin 12 / 23 (ustekinumab) treatments starting from the baseline, and the clinical remission of the patients was evaluated at 14 weeks. A clinical remission was defined as an HBI or SCCAI score ≤ 2 points.

[0099] The baseline fecal metagenomic data of patients in different subgroups were analyzed, and the ROC curve was plotted. The specific results are as Figures 6-7 shown. Table 3 shows the baseline characteristic results of patients with ulcerative colitis. Figure 6 For the prediction results of the response to anti-tumor necrosis factor treatment in patients with ulcerative colitis (11 cases), the AUC was 0.93, the sensitivity was 0.67, the specificity was 1.00, the accuracy was 0.82, the PPV was 1.00, and the NPV was 0.71, indicating that the model had good predictive value for the response to anti-tumor necrosis factor treatment in patients with ulcerative colitis.

[0100] Table 3. Baseline Characteristics of Patients with Ulcerative Colitis

[0101]

[0102] Table 4 shows the baseline characteristic results of patients with inflammatory bowel disease. Figure 7 For the prediction results of the response to ustekinumab treatment in patients with inflammatory bowel disease (10 cases, including patients with ulcerative colitis or Crohn's disease), the AUC was 0.95, the sensitivity was 0.86, the specificity was 1.00, the accuracy was 0.90, the PPV was 1.00, and the NPV was 0.75, indicating that the model also had relatively ideal predictive performance for the therapeutic efficacy of ustekinumab.

[0103] Table 4. Baseline Characteristics of Patients with Inflammatory Bowel Disease

[0104]

[0105]

[0106] Note: There was one case of UC in the validation cohort, and the scope of colonic lesions was not counted. The HBI index is the Harvey - Bradshaw Index (HBI), a practical tool for evaluating the disease severity (activity) of patients with Crohn's disease.

[0107] Based on the above validation results, this model provides strong support for optimizing the individualized treatment regimens of anti - tumor necrosis factor and ustekinumab for patients with inflammatory bowel disease, and provides new ideas for the exploration of related microbial markers.

[0108] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0109] In addition, it should be noted that, in the case of no contradiction, the various specific technical features described in the above specific embodiments can be combined in any appropriate manner. To avoid unnecessary repetition, the present invention will not separately describe various possible combination methods.

Claims

1. An application of an intestinal microbial marker in the preparation of a product for predicting the efficacy of a drug on inflammatory bowel disease, characterized in that: The intestinal microbial marker is selected from one or more of Faecalibacterium prausnitzii, Blautia massiliensis and Phascolarctobacterium faecium.

2. The use according to claim 1, characterized in that: The intestinal microbial marker is selected from a combination of three strains of Faecalibacterium prausnitzii, Blautia massiliensis and Phascolarctobacterium faecium.

3. The use according to claim 1, characterized in that: The inflammatory bowel disease is selected from one or both of ulcerative colitis and Crohn's disease.

4. The use according to claim 1, characterized in that: The drug for treating inflammatory bowel disease is selected from one or more of anti-tumor necrosis factor, interleukin (IL)-12 / 23 inhibitor, and 5-aminosalicylic acid.

5. The use according to claim 1, characterized in that: The inflammatory bowel disease is ulcerative colitis, and the drug is 5-aminosalicylic acid; or the inflammatory bowel disease is ulcerative colitis, and the drug is an anti-tumor necrosis factor; Or the inflammatory bowel disease is ulcerative colitis or Crohn's disease, and the drug is an interleukin-12 / 23 inhibitor.

6. A kit for predicting the efficacy of a drug on inflammatory bowel disease, characterized in that: The kit comprises a product for detecting the intestinal microbial marker according to claim 1.

7. A prediction model for the efficacy of a drug on inflammatory bowel disease, characterized in that: The prediction model uses the intestinal microbial markers described in claim 1 as variables.

8. The prediction model according to claim 7, characterized in that The prediction model is a random forest model; Preferably, the variables of the prediction model are a combination of Faecalibacterium prausnitzii, Blautia massiliensis and Phascolarctobacterium faecium.

9. A system for predicting the efficacy of a drug for inflammatory bowel disease, characterized in that: The system comprises: A data processing module, for receiving or inputting intestinal microbial abundance data in stool samples of patients with inflammatory bowel disease, wherein the intestinal microbes include the intestinal microbial markers according to claim 1; The judgment and output module is used to obtain and output the prediction result of whether the drug treatment of the inflammatory bowel disease patient is effective through the prediction model described in claim 7 after the receiving or input is completed.

10. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor is used to execute the computer program to implement the functions of the system described in claim 9.

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