Enteric microbial marker for liver cirrhosis, application of intestinal microbial marker and method for constructing liver cirrhosis detection model

Abstract

The invention provides a liver cirrhosis intestinal microbial marker, application thereof and a method for constructing a liver cirrhosis detection model, and belongs to the technical field of biomedicine. The invention provides the intestinal microbe marker for detecting liver cirrhosis, and the intestinal microbe marker composition of a patient with liver cirrhosis can be analyzed, the change of the intestinal microbe marker of the patient can be monitored, the adjustment of the intestinal microbe of the patient with liver cirrhosis can be guided, and a targeted treatment method for the intestinal microbe can be developed; different treatment schemes are provided for different liver cirrhosis patients, normal intestinal flora of the liver cirrhosis patients is recovered, and a good liver cirrhosis treatment effect is achieved. The liver cirrhosis detection model constructed by using the intestinal microbial marker for detecting liver cirrhosis can more accurately identify the intestinal flora difference between a liver cirrhosis patient and healthy people. The AUC value of the liver cirrhosis detection model reaches 0.9878, which indicates that the liver cirrhosis detection model has good performance when distinguishing positive and negative samples.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a liver cirrhosis intestinal microbial marker and its application and a method for constructing a liver cirrhosis detection model. Background Art

[0002] Cirrhosis of the liver Cirrhosis of the liver is a chronic, progressive disease that occurs mainly on the basis of extensive necrosis of liver cells. It is caused by diffuse proliferation of liver fibrosis, forming various problems such as nodules and pseudolobules, so that the normal structure and blood supply of the liver are destroyed. The symptoms of cirrhosis vary. In the early stage, there may be no symptoms, or there may be non-specific manifestations such as fatigue, loss of appetite, and diarrhea. As the disease progresses, patients may experience symptoms such as jaundice, weight loss, fatigue, ascites, and coma. At present, the incidence of cirrhosis is on the rise worldwide, especially in some specific regions and populations, such as alcoholics, chronic hepatitis B and C infections, obese and diabetic patients, etc., the incidence is relatively high.

[0003] Existing methods for diagnosing cirrhosis include laboratory tests, imaging tests, and liver puncture biopsy. However, each diagnostic method has its limitations. For example, abnormalities in liver function indicators are not unique to cirrhosis, and other liver diseases may also cause similar changes, and the specificity is not high. Abnormalities in coagulation function and blood routine may also be affected by multiple factors, such as drugs, malnutrition, etc., and may not accurately reflect the severity of cirrhosis. The tissue obtained by puncture is only a small part of the liver and may not fully represent the pathological condition of the entire liver, and there is a sampling error.

[0004] Studies have found that in the pathological state of cirrhosis, intestinal microorganisms can undergo quantitative and qualitative changes, further aggravating the progression of cirrhosis. Therefore, intestinal microorganisms are crucial for the diagnosis and treatment of cirrhosis. Summary of the invention

[0005] The purpose of the present invention is to provide a cirrhosis intestinal microbial marker and its application and a method for constructing a cirrhosis detection model. By means of metagenomic sequencing technology, the composition and changes of intestinal microorganisms in patients with cirrhosis can be comprehensively analyzed, and characteristic intestinal microorganisms of cirrhosis can be discovered to serve as an auxiliary diagnosis for early detection of cirrhosis, and further guide the development of targeted treatment methods for intestinal microorganisms to delay the progression of cirrhosis and prevent the occurrence of complications.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] The present invention provides an intestinal microbial marker for liver cirrhosis, characterized in that the intestinal microbial marker comprises the following species: Eubacterium_hallii, Blautia_wexlerae, Anaerostipes_hadrus, Bifidobacterium_longum, Blautia_obeum, Bifidobacterium_pseudocatenulatum, Prevotella_copri, Eubacterium_ramulus, Dorea_formicigenerans, Dorea_longicatena, and Agathobaculum_butyriciproducens.

[0008] The present invention also provides an application of an intestinal microbial marker for cirrhosis, characterized in that the application includes a product for preparing a diagnosis of whether a patient has cirrhosis or is at risk of cirrhosis, a target for the assessment and treatment of cirrhosis, and a construction of a cirrhosis detection model; the product includes any one of a detection reagent, a detection kit, and a detection chip.

[0009] The present invention also provides a method for constructing a liver cirrhosis detection model using the liver cirrhosis intestinal microbial markers described in claim 1, characterized in that it comprises the following steps:

[0010] (1) Total DNA was extracted from the feces of patients with cirrhosis and healthy people, and high-throughput sequencing was performed after pretreatment. The sequencing results were annotated with species and analyzed for composition, gene abundance, function, and difference;

[0011] (2) Using random forest to screen liver cirrhosis microbial markers, constructing a random forest model, optimizing parameters, and training and testing the constructed random forest model with training and test sets to obtain a liver cirrhosis detection model;

[0012] (3) The metagenome and abundance information of the patient to be tested are input into the liver cirrhosis detection model for identification.

[0013] Furthermore, in step (1), the species annotation and composition spectrum analysis includes: alpha diversity analysis, beta diversity analysis; flora sample composition, sample heatmap, and species interaction network analysis.

[0014] Furthermore, in step (1), the gene abundance analysis includes gene Core-Pan curve, gene dilution curve, and correlation analysis between samples of gene numbers.

[0015] Furthermore, in step (1), the functional annotation includes: KEGG data rate functional annotation analysis, HUMAnN3: quantitative pathway abundance, KO functional abundance and analysis, Module functional abundance and analysis, Pathway functional abundance and analysis.

[0016] Furthermore, in step (1), the difference analysis includes: Lefse analysis, dominant species difference, multivariate variance adonis, MaAsLin, and rank sum test difference.

[0017] Furthermore, in step (2), the parameters for optimizing the parameters are determined according to GridSearchCV.

[0018] The present invention also provides an application of a liver cirrhosis intestinal microbial marker in the preparation of a drug for treating liver cirrhosis.

[0019] The beneficial effects of the present invention compared with the prior art are:

[0020] (1) The present invention is based on the metagenomic data of clinical cirrhosis patients and healthy people's samples, extracts features related to cirrhosis from the data, including differential microbial species abundance, metabolic pathway activity, etc., and uses a random forest algorithm to construct a cirrhosis detection model as an auxiliary diagnosis for early cirrhosis detection. The cirrhosis detection model of the present invention can more accurately identify the differences in intestinal flora between cirrhosis patients and healthy people. The AUC value of the cirrhosis detection model of the present invention reached 0.9878, indicating that the cirrhosis detection model performs well in distinguishing positive and negative samples. The CI ranges from 0.9743 to 1, and a reliable evaluation of the AUC value is performed, indicating the stability of the model performance.

[0021] (2) The present invention provides intestinal microbial markers for detecting liver cirrhosis. By analyzing the composition of intestinal microbial markers in patients with liver cirrhosis and monitoring changes in the intestinal microbial markers of patients, the present invention can guide the adjustment of intestinal microorganisms in patients with liver cirrhosis, develop targeted treatment methods for intestinal microorganisms, provide different treatment plans for different patients with liver cirrhosis, restore the normal intestinal flora of patients with liver cirrhosis, and achieve good treatment effects for liver cirrhosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] Figure 1 A flow chart for constructing a liver cirrhosis detection model for Example 1;

[0024] Figure 2 This is a graph showing the results of ten cross-validation runs in Example 1;

[0025] Figure 3 It is the "Mean DecreaseAccuracy" indicator in the random forest model of Example 1;

[0026] Figure 4 is the ROC curve in Example 1; Figure 5 This is the judgment standard of the liver cirrhosis detection model in Example 1. DETAILED DESCRIPTION

[0027] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0028] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0029] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0030] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to the skilled artisan. The present invention description and examples are exemplary only.

[0031] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0032] The present invention provides a method for constructing a liver cirrhosis detection model, which specifically comprises the following steps:

[0033] (1) Total DNA was extracted from the feces of patients with cirrhosis and healthy people, and high-throughput sequencing was performed after pretreatment. The sequencing results were annotated with species and analyzed for composition, gene abundance, function, and difference;

[0034] The species annotation and composition spectrum analysis include:

[0035] Alpha diversity: Shannon, Simpson, Insimpson index analysis;

[0036] beta diversity: Bray-CurtisPCoa / PCA / NMDS / UPGMA analysis;

[0037] Bacteria sample composition, sample heatmap;

[0038] Species interaction network analysis.

[0039] The gene abundance analysis includes gene Core-Pan curve, gene dilution curve, and correlation analysis between samples of gene numbers.

[0040] The functional annotations include: KEGG data rate functional annotation analysis, HUMAnN3: quantitative pathway abundance, KO functional abundance and analysis, Module functional abundance and analysis, Pathway functional abundance and analysis.

[0041] The difference analysis includes: Lefse analysis, dominant species difference, multivariate variance adonis, MaAsLin, and rank sum test difference.

[0042] (2) Using random forest to screen liver cirrhosis microbial markers, constructing a random forest model, optimizing parameters, and training and testing the constructed random forest model with training and test sets to obtain a liver cirrhosis detection model;

[0043] Among them, the screened intestinal microbial markers include the following species: Bifidobacterium_hallii, Blautia_wexlerae, Anaerostipes_hadrus, Bifidobacterium_longum, Blautia_obeum, Bifidobacterium_pseudocatenulatum, Prevotella_copri, Eubacterium_ramulus, Dorea_formicigenerans, Dorea_longicatena, Agathobaculum_butyriciproducens

[0044] (3) The metagenome and abundance information of the patient to be tested are input into the liver cirrhosis detection model for identification.

[0045] Example 1

[0046] Embodiment 1 of the present invention provides a method for constructing a liver cirrhosis detection model, which specifically includes the following steps:

[0047] Fecal samples were collected from 13 patients with cirrhosis and 227 healthy controls using stool collection boxes. The samples were collected to ensure that the patients had not received antibiotics or other related treatments that might affect the intestinal flora. When collecting fecal samples from patients with cirrhosis and normal controls, the urine was emptied first to avoid urine contamination. The internal fecal samples were taken with a sterile spoon, and the samples were stored in a special fecal box at -80°C.

[0048] DNA was extracted using Qiagen's DNeasy PowerSoil Kit to remove proteins and other impurities to obtain pure DNA. After extraction, high molecular weight DNA is randomly broken into fragments of specific lengths, generally about 300 bp, using an ultrasonic disruptor; the ends of the fragmented DNA are repaired to make them blunt-ended; specific adapter sequences are connected to both ends of the repaired DNA fragments, and excess adapters, primers and other impurities are removed by magnetic bead or column purification; specific primers are used to perform PCR amplification on DNA fragments with adapters to enrich a sufficient amount of library fragments; fastp software is used to perform quality control on the sequencing data, including removing adapters and low-quality sequences; Bowtie2 is used to filter out reads that may be derived from the host; MetaPhlAn4 software is used to annotate the metagenomic sequencing data for species, and the composition of the microbial community is analyzed for the metagenomic data of the sequencing data to obtain relative abundance information; HUMAnN3 is used to identify the species-level functional composition of known species in the population by comparing the pan-genome through a tiered search strategy, complete the quantitative gene family and pathway abundance, and quickly obtain diverse species and functional composition; Bowtie2 and other statistical analysis software models (GMM) are used to compare and analyze the metagenomic data. Finally, the difference analysis step was performed by Lefse analysis, comparison of metabolic species differences, multivariate analysis of variance (such as adonis and MAdLIN), and random forest methods ( Figure 1), conducted an in-depth study of the differences between samples, and found 12 bacterial genera with significant changes in biomarkers between patients with non-alcoholic fatty liver disease and healthy people, including the following genera: Eubacterium_hallii, Blautia_wexlerae, Fusicatenibacter_saccharivorans, Anaerostipes_hadrus, Bifidobacterium_longum, Blautia_obeum, Bifidobacterium_pseudocatenulatum, Prevotella_copri, Eubacterium_ramulus, Dorea_formicigenerans, Dorea_longicatena, Agathobaculum_butyriciproducen.

[0049] The training set of patients with cirrhosis and healthy people was used to train the random forest model using the python software sklearn.Ensemble.RandomForestClassifier module to obtain the cirrhosis detection model; the model training parameters were set as follows: gradient n_estimators = [100,500,1000]; max_depth = [1,2,3,7,9]; max_features = ["log2","sqrt"].

[0050] The stability and reliability of the liver cirrhosis detection model were verified by ten-fold cross validation. The python software GridSearchCV module was used to search for the optimal parameters. The random forest model was constructed using the optimal parameters. The liver cirrhosis detection model was obtained by training and testing with the training set and test set. The results are shown in the figure. Figure 2 As shown in Table 1.

[0051] Depend on Figure 2It can be seen that the vertical axis reflects the value of the cross-validation error, which ranges roughly from 0.00 to 0.10. There are several curves in the line graph, which represent different variable selection methods. The black curve clearly shows the trend of cross-validation error as the number of variables increases. This curve initially shows a downward trend, indicating that in the process of increasing variables, the prediction error of the model is gradually decreasing. Both the model training set and the test set have high accuracy, indicating that the model results are good. The retention performance of 12 bacterial genera, including Eubacterium_hallii, Blautia_wexlerae, Anaerostipes_hadrus, Bifidobacterium_longum, Blautia_obeum, Bifidobacterium_pseudocatenulatum, Prevotella_copri, Eubacterium_ramulus, Dorea_formicigenerans, Dorea_longicatena, and Agathobaculum_butyriciproducen, is the best.

[0052] Table 1 Indicators of liver cirrhosis detection model

[0053]

[0054] In Table 1, "Mean DecreaseAccuracy" and "Mean Decrease Gini" are two important indicators in the random forest model. "Mean Decrease Gini" calculates the impact of each variable on the heterogeneity of the observed values ​​at each node of the classification tree, thereby comparing the importance of the variable. The larger the value, the greater the importance of the variable. "MeanDecreaseAccuracy" indicates the degree of reduction in the prediction accuracy of the random forest. The larger the value, the greater the importance of the variable (see the results). Figure 3 ).

[0055] The horizontal axis (X-axis) of the ROC curve represents 1-Specificity, that is, the false positive rate, which ranges from 0 to 1, reflecting the proportion of negative examples that the model mistakenly predicts as positive examples. The vertical axis (Y-axis) represents Sensitivity, that is, the true positive rate, which measures the ability of the model to correctly identify positive examples. The results are shown in Figure 4 .

[0056] exist Figure 4In the figure, the red solid line clearly depicts the ROC curve of the model, and the shaded part represents the confidence interval (CI). The ROC curve in the present invention is close to the upper left corner, and has a higher true positive rate at each threshold. The area under the curve (AUC) is close to 1, indicating that the classification performance of the model is better. The AUC value of the present invention reaches 0.9878, indicating that the classifier performs well in distinguishing positive and negative samples. The CI ranges from 0.9743 to 1, which provides a reliable evaluation of the AUC value, indicating the stability of the model performance.

[0057] The metagenome and abundance information of the patient to be tested are input into the liver cirrhosis detection model, and the patient is judged whether he or she is ill according to the predicted score. The judgment criteria are: risk value 0-0.5, low risk; 0.5-0.7 medium risk; 0.7-1 high risk. The results are shown in Figure 5 .

[0058] 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 liver cirrhosis intestinal microbial marker, characterized in that: The intestinal microbial markers include the following species: Eubacterium_hallii, Blautia_wexlerae, Anaerostipes_hadrus, Bifidobacterium_longum, Blautia_obeum, Bifidobacterium_pseudocatenulatum, Prevotella_copri, Eubacterium_ramulus, Dorea_formicigenerans, Dorea_longicatena, and Agathobaculum_butyriciproducens.

2. A use of the intestinal microbial marker for liver cirrhosis according to claim 1, characterized in that: The application includes the preparation of products for diagnosing whether a patient has cirrhosis or is at risk of cirrhosis, using them as targets for the assessment and treatment of cirrhosis, and constructing a cirrhosis detection model; the product includes any one of a detection reagent, a detection kit, and a detection chip.

3. A method for constructing a liver cirrhosis detection model using the liver cirrhosis intestinal microbial markers according to claim 1, characterized in that: The steps include: (1) Total DNA was extracted from the feces of patients with cirrhosis and healthy people, and high-throughput sequencing was performed after pretreatment. The sequencing results were annotated with species and analyzed for composition, gene abundance, function annotation, and difference analysis; (2) Use random forest to screen liver cirrhosis microbial markers, construct a random forest model, optimize parameters, and train and test the constructed random forest model using training sets and test sets to obtain a liver cirrhosis detection model; (3) The metagenome and abundance information of the patient to be tested are input into the liver cirrhosis detection model for identification.

4. The method for constructing a liver cirrhosis detection model according to claim 3, characterized in that: In step (1), the species annotation and composition spectrum analysis includes: alpha diversity analysis, beta diversity analysis; microbial sample composition, sample heatmap, and species interaction network analysis.

5. The method for constructing a liver cirrhosis detection model according to claim 3, characterized in that: In step (1), the gene abundance analysis includes gene Core-Pan curve, gene dilution curve, and correlation analysis between gene numbers in samples.

6. The method for constructing a liver cirrhosis detection model according to claim 3, characterized in that: In step (1), the functional annotation includes: KEGG data rate functional annotation analysis, HUMAnN3: quantitative pathway abundance, KO functional abundance and analysis, Module functional abundance and analysis, Pathway functional abundance and analysis.

7. The method for constructing a liver cirrhosis detection model according to claim 3, characterized in that: In step (1), the difference analysis includes: Lefse analysis, dominant species difference, multivariate variance adonis, MaAsLin, and rank sum test difference.

8. The method for constructing a liver cirrhosis detection model according to claim 3, characterized in that: In step (2), the parameters for optimizing the parameters are determined according to GridSearchCV.

9. Application of an intestinal microbial marker for cirrhosis in the preparation of drugs for the treatment of cirrhosis.

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