Biomarker for screening intestinal flora transplantation donors and application thereof

Abstract

The invention relates to a biomarker for screening intestinal flora transplantation donors and application of the biomarker. The biomarker comprises intestinal flora and metabolites thereof, the intestinal flora comprises phylum firmicalis, bacteroides and the like, and the metabolites comprise short-chain fatty acid, tryptophan, indole, 5-hydroxytryptamine, lipopolysaccharide and the like. According to the method, the intestinal flora of different crowds is deeply analyzed, the relationship between each related index of the intestinal flora and the intestinal flora transplantation effect is studied, sufficient clinical verification is carried out, a biomarker for screening the intestinal flora transplantation donor is excavated, and a standard for judging whether the intestinal flora transplantation donor is qualified or not is established; and a new method and a new idea are provided for efficiently and accurately screening intestinal flora transplantation donors.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biomedical technology, and relates to a biomarker for screening donors for intestinal flora transplantation and application thereof. BACKGROUND

[0002] Human Microbiota Transplantation (HMT or FMT) is a new treatment method for improving intestinal and extra-intestinal diseases by transplanting functional flora in healthy human feces into the gastrointestinal tract of patients to reconstruct intestinal flora. The donor refers to a person who provides feces for flora transplantation. The heterogenicity of donor feces has a great influence on the effect of intestinal flora transplantation. Only donors who have been strictly screened and scientifically evaluated can ensure the efficiency and safety of intestinal flora transplantation.

[0003] In 2002, China introduced the "Chinese Expert Consensus on Clinical Application of Intestinal Flora Transplantation (2022 Edition)", which provides guidance from six key dimensions: physiological health, psychological status, personal medical history, flora stability, long-term donor sustainability, and nutrition. The expert consensus specifies the general process of donor screening: ① Establish a multidisciplinary donor screening team; ② Network screening; ③ On-site interview and questionnaire screening; ④ Flora condition assessment; ⑤ Clinical physical examination; ⑥ Donor management. However, the expert consensus does not specify the method of flora condition assessment, which is the most critical step in donor screening. Moreover, with the deterioration of the environment, the increase in eating out, and the increase in work and study pressure, the quality of intestinal flora in the population is generally in a sub-healthy state. Therefore, determining what constitutes healthy flora suitable for disease transplantation is crucial and has become one of the most concerned issues in the field of intestinal flora transplantation.

[0004] CN111681733A discloses a method for screening donors for intestinal flora transplantation, which includes the following steps: (1) informing the donor about the concept of flora transplantation, the therapeutic value of flora transplantation for patients, the indications for flora transplantation, and the current status of flora transplantation at home and abroad; (2) filling out a questionnaire including basic information, medical history, personal history, allergy history, gastrointestinal symptom score, dietary habits, and personality classification, and using a professional analysis webpage to analyze the health aspects of the donor; (3) conducting clinical evaluation on the donors screened in step (2); (4) conducting gastrointestinal pathogenic bacteria detection on the donors screened in step (3); (5) conducting blood tests on the donors screened in step (4); (6) determining the donor based on the blood test results, and the qualified personnel are the healthy donors for this time. The gastrointestinal pathogenic bacteria detection in step (4) includes: stool routine, fecal occult blood, intestinal parasites, pathogenic viruses, common pathogenic bacteria, multi-drug resistant bacteria, and Helicobacter pylori.

[0005] In summary, the current donor screening method is still mainly conventional pathogenic bacteria detection and blood detection, and there is still a lack of a method that can accurately evaluate and predict the effect of donor intestinal flora transplantation after transplantation, making it difficult to achieve precise screening. SUMMARY

[0006] In view of the deficiencies of the prior art and actual needs, the present application provides a biomarker for screening intestinal flora transplantation donors and an application thereof, so as to realize efficient and accurate screening of intestinal flora transplantation donors.

[0007] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0008] In a first aspect, the present application provides a biomarker for screening intestinal flora transplantation donors, which comprises intestinal flora and metabolites thereof, the intestinal flora comprising Firmicutes and Bacteroidetes, the Bacteroidetes comprising Bacteroides and Prevotella, the Bacteroides comprising Bacteroides thetaiotaomicron, Bacteroides uniformis, Bacteroides plebeius, Bacteroides vulgatus, Bacteroides ovatus and Bacteroides stercoris, and the metabolites comprising short-chain fatty acids, tryptophan, indole, 5-hydroxytryptamine (5-HT) and lipopolysaccharide (LPS), the short-chain fatty acids comprising acetic acid, propionic acid and butyric acid.

[0009] In the present application, the intestinal flora of different populations is analyzed in depth, the relationship between each relevant index of the intestinal flora and the effect of intestinal flora transplantation is studied, and sufficient clinical verification is carried out, so as to mine biomarkers for screening intestinal flora transplantation donors and provide new methods and new ideas for efficient and accurate screening of intestinal flora transplantation donors.

[0010] In a second aspect, the present application provides an application of the biomarker for screening intestinal flora transplantation donors and / or its detection reagent of the first aspect in the preparation of a product for screening intestinal flora transplantation donors.

[0011] In a third aspect, the present application provides a kit for screening intestinal flora transplantation donors, which comprises a reagent for detecting the biomarker for screening intestinal flora transplantation donors of claim 1.

[0012] Preferably, the reagent comprises a reagent for detecting the abundance of the gut flora and its metabolites, including 16S rRNA gene sequencing reagent.

[0013] In a fourth aspect, the present application provides the use of the biomarker for screening donors of gut flora transplantation and / or the detection reagent thereof in screening donors of gut flora transplantation.

[0014] Preferably, the use comprises screening donors for treating lung adenocarcinoma by gut flora transplantation.

[0015] In a fifth aspect, the present application provides a method for screening donors of gut flora transplantation, comprising:

[0016] detecting the abundance of the biomarker for screening donors of gut flora transplantation in the feces of the donor to be screened according to the first aspect; and determining whether the donor is qualified according to the detection result.

[0017] Preferably, the standard for determining whether the donor is qualified comprises:

[0018] (1) the ratio of the abundance of Firmicutes to the abundance of Bacteroidetes is 0.6-1.5;

[0019] (2) the intestinal type is Bacteroides type;

[0020] (3) the abundance of Prevotella is <10%;

[0021] (4) the Bacteroides contains Bacteroides thetaiotaomicron, Bacteroides uniformis, Bacteroides vulgatus, Bacteroides caccae, Bacteroides ovatus and Bacteroides dorei;

[0022] (5) the abundance of acetic acid is 15-95, the abundance of propionic acid is 15-90, the abundance of butyric acid is 15-95, the abundance of tryptophan is 15-95, the abundance of indole is 15-90, the abundance of 5-hydroxytryptamine is 15-95 (preferably 15-50), and the abundance of lipopolysaccharide is 0-85.

[0023] In the present application, the abundance of gut flora can be determined by analyzing the results of 16S rRNA gene sequencing, and the abundance of related metabolites can be determined by mass spectrometry.

[0024] In a sixth aspect, the present application provides a model for screening donors of gut flora transplantation, which is used to perform the steps in the method for screening donors of gut flora transplantation according to the fifth aspect, and comprises a detection unit and a determination unit.

[0025] The detection unit is used to execute the steps of obtaining the abundance of the biomarker for screening the donor of the intestinal flora transplantation in the feces of the donor to be screened; and the judgment unit is used to execute the steps of calculating and judging according to the result of the detection unit.

[0026] The standard for judging a qualified donor includes:

[0027] (1) The ratio of the abundance of Firmicutes to the abundance of Bacteroidetes is 0.6-1.5;

[0028] (2) The intestinal type is Bacteroides type;

[0029] (3) The abundance of Prevotella is less than 10%;

[0030] (4) The Bacteroides contains Bacteroides thetaiotaomicron, Bacteroides uniformis, Bacteroides vulgatus, Bacteroides caccae, Bacteroides ovatus and Bacteroides dorei;

[0031] (5) The abundance of acetic acid is 15-95, the abundance of propionic acid is 15-90, the abundance of butyric acid is 15-95, the abundance of tryptophan is 15-95, the abundance of indole is 15-90, the abundance of 5-hydroxytryptamine is 15-95 (preferably 15-50), and the abundance of lipopolysaccharide is 0-85.

[0032] In a seventh aspect, the present application provides an electronic device, comprising one or more processors and a memory for storing executable instructions, wherein the one or more processors are configured to invoke the executable instructions stored in the memory to realize the function of the model for screening the donor of the intestinal flora transplantation according to the sixth aspect.

[0033] In an eighth aspect, the present application provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions are executed by a processor to realize the function of the model for screening the donor of the intestinal flora transplantation according to the sixth aspect.

[0034] Compared with the prior art, the present application has at least the following beneficial effects:

[0035] In the present application, the intestinal flora of different populations is analyzed in depth, the relationship between the relevant indicators of the intestinal flora and the effect of the intestinal flora transplantation is studied, and sufficient clinical verification is performed, the biomarker for screening the donor of the intestinal flora transplantation is mined, and the standard for judging whether the donor of the intestinal flora transplantation is qualified is established, thereby providing a new method and a new idea for efficiently and accurately screening the donor of the intestinal flora transplantation. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 Figure 1 is a result chart of the intestinal flora community structure of a qualified donor volunteer S001.

[0037] Figure 2 Figure for gut microbiota community structure results for eligible donor volunteer S006.

[0038] Figure 3 Figure for gut microbiota community structure results for eligible donor volunteer S009.

[0039] Figure 4 Figure for gut microbiota community structure results for eligible donor volunteer S0010.

[0040] Figure 5 Figure for gut microbiota community structure results for eligible donor volunteer S0011.

[0041] Figure 6 Figure for gut microbiota community structure results for eligible donor volunteer S0018.

[0042] Figure 7 Figure for gut microbiota community structure results for eligible donor volunteer S001.

[0043] Figure 8 Figure for gut microbiota community structure results for eligible donor volunteer S006.

[0044] Figure 9 Figure for gut microbiota community structure results for eligible donor volunteer S009.

[0045] Figure 10 Figure for gut microbiota community structure results for eligible donor volunteer S0010.

[0046] Figure 11 Figure for gut microbiota community structure results for eligible donor volunteer S0011.

[0047] Figure 12 Figure for gut microbiota community structure results for eligible donor volunteer S0018.

[0048] Figure 13 Figure for donor continuous alpha-diversity plot, with the vertical axis as the alpha- diversity index.

[0049] Figure 14 Figure for the schematic diagram of the treatment procedure of gut microbiota transplantation combined with PD-1 antibody and chemotherapy.

[0050] Figure 15 Figure for the schematic diagram of the total treatment procedure of the treated patient.

[0051] Figure 16 Figure for the chest CT tumor lesion of Case 1.

[0052] Figure 17 Figure for the chest CT tumor lesion of Case 2.

[0053] Figure 18Figure 3. Chest CT of the tumor lesion in the medical record.

[0054] Figure 19 Figure 4. Intestinal flora structure results of the R001 patient after intestinal flora transplantation.

[0055] Figure 20 Figure 5. Intestinal flora structure results of the R002 patient after intestinal flora transplantation.

[0056] Figure 21 Figure 6. Intestinal flora structure results of the R003 patient after intestinal flora transplantation.

[0057] Figure 22 Figure 7. Intestinal flora structure results of the R004 patient after intestinal flora transplantation.

[0058] Figure 23 Figure 8. Intestinal flora structure results of the R005 patient after intestinal flora transplantation.

[0059] Figure 24 Figure 9. Intestinal flora structure results of the R006 patient after intestinal flora transplantation.

[0060] Figure 25 Figure 10. Intestinal flora structure results of the R008 patient after intestinal flora transplantation.

[0061] Figure 26 Figure 11. Intestinal flora structure results of the R009 patient after intestinal flora transplantation.

[0062] Figure 27 Figure 12. Intestinal flora structure results of the R010 patient after intestinal flora transplantation.

[0063] Figure 28 Figure 13. Intestinal flora structure results of the R011 patient after intestinal flora transplantation.

[0064] Figure 29 Figure 14. Beta diversity distribution of intestinal flora structure of the recipient after flora transplantation. A, beta diversity box plot; Bray-Curtis: Bray-Curtis dissimilarity; Same donor: beta diversity of flora of different time points of the same healthy donor; Recipient after FMT: beta diversity of flora of the patient after transplantation; Prior vs post-FMT: beta diversity of flora of the same patient before and after transplantation; Donor vs Recipient: beta diversity of flora of the donor and the patient after transplantation; Different donors: beta diversity of flora of different donors; Different recipients: beta diversity of flora of different recipients. B, beta diversity network plot.

[0065] Figure 30Figure 2. Analysis of the efficiency of strain-level microbiota transplantation. A, frequency distribution histogram; B, scatter plot; C, stacked bar chart.

[0066] Figure 31 Figure 3. Results of the structure of intestinal microbiota of the recipient before and after microbiota transplantation.

[0067] Figure 32A Figure 4. Beta diversity box plot. Same donor: comparison of the beta diversity of the microbiota of different time points of the same healthy donor; Different donors: comparison of the beta diversity of the microbiota of different donors; Recipient after FMT: comparison of the beta diversity of the microbiota of the patient after transplantation; Different recipients: comparison of the beta diversity of the microbiota of different recipients; Donor vs Recipient: comparison of the beta diversity of the microbiota of the donor and the patient after transplantation; Prior vs post-FMT: comparison of the beta diversity of the microbiota of the same patient before and after transplantation; Sharing donors: comparison of the beta diversity of the microbiota of the same donor comparison group.

[0068] Figure 32B Figure 5. Scatter plot.

[0069] Figure 32C Figure 6. Stacked bar chart.

[0070] Figure 32D Figure 7. Strain sharing network. DETAILED DESCRIPTION

[0071] The technical solutions of the present application will be further described below in combination with the drawings and through specific embodiments. However, the following examples are only simple examples of the present application and do not represent or limit the protection scope of the present application, and the protection scope of the present application is subject to the claims.

[0072] If a specific technology or condition is not specified in the examples, the technology or condition described in the literature in the art or according to the product manual is used. If the manufacturer of the reagent or instrument is not specified, it is a conventional product that can be purchased through a regular channel.

[0073] Enterotype is a classification of human intestinal microbiota based on dominant bacterial species and functional differences, mainly reflecting the composition characteristics of intestinal microorganisms, and is related to metabolism, immunity and disease risk. The currently recognized enterotypes include Bacteroides, Prevotella and Ruminococcus, and different enterotypes have important influences on nutrient absorption, drug response, etc. Bacteroides: the intestinal microbiota is dominated by Bacteroides. Prevotella: dominated by Prevotella. Ruminococcus: dominated by Ruminococcus.

[0074] In specific embodiments of the present application, the method for establishing the normal value of the intestinal microflora and the abundance of its metabolites is as follows: analyzing a large amount of sample data and a control database (such as the detection data provided by Hangzhou Guhe Information Technology Co., Ltd.), and establishing the normal population distribution range of intestinal flora and its metabolites by analyzing the diversity index, relative abundance and community structure of intestinal flora.

[0075] Example 1

[0076] In this example, the fecal samples of the recruited volunteers were analyzed.

[0077] 1. The enrollment of volunteers is shown in Tables 1, 2 and 3.

[0078] Table 1 Information of centenarians enrolled

[0079] Number Gender Age B001 Female 101 B002 Female 100 B003 Female 101 B004 Female 104 B005 Female 100

[0080] Table 2 Sub-healthy population enrolled

[0081]

[0082] Table 3 Patients with diseases enrolled

[0083]

[0084] 2. 16S rRNA fecal detection sample collection, detection and analysis:

[0085] (1) Fecal sample collection

[0086] The fecal sample collection kit is used, including a cotton swab, a collection paper set containing fecal collection paper, and a collection tube containing a protective solution (the collection tube containing a protective solution is purchased from Hangzhou Guhe Biological, and the others are purchased from Guangzhou Chengge Biological). The fecal sample is collected according to the requirements in the instruction manual of the collection kit. The brief collection process is as follows:

[0087] i. Before sample collection, empty the bladder;

[0088] ii. Open the package of the fecal collection kit and take out the contents;

[0089] iii. Place the fecal collection paper set in the middle of the toilet seat and defecate on the fecal collection paper;

[0090] iv. Use the cotton swab to push away the surface of the feces, and take the middle part without air contact and the size of a soybean to the collection tube containing the protective solution;

[0091] v. Tighten the collection tube cap, and turn the collection tube upside down for half a minute until no feces are visible in the collection tube;

[0092] vi. Collect the fecal collection tube and flush the feces away with the sampling paper.

[0093] (2) Sample detection

[0094] 1) Sample DNA extraction

[0095] All microbial genomic DNA in the fecal sample was extracted using a bead beating extraction method and a column membrane purification method. The concentration and purity of the extracted DNA were detected, and the DNA sample was stored at -20°C.

[0096] 2) Intestinal flora 16S rRNA gene sequencing

[0097] The concentration of the DNA sample was detected using a Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA), and a library construction kit was used to construct a sequencing library. 20-30 ng of DNA was used as a template to amplify the two highly variable regions including V3 and V4 on the prokaryotic 16S rDNA. The V3 and V4 regions were amplified using an upstream primer containing the sequence "CCTACGGRRBGCASCAGKVRVGAAT" and a downstream primer containing the sequence "GGACTACNVGGGTWTCTAATCC". In addition, an Index-bearing adapter was added to the end of the PCR product of 16S rDNA by PCR in order to perform NGS sequencing. The library quality was detected using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA), and the library concentration was detected by Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA). After mixing the DNA library, 2x300 bp paired-end sequencing (PE) was performed according to the instrument manual of Illumina MiSeq (Illumina, San Diego, CA, USA), and the sequence information was read by the MiSeq Control Software (MCS) of the MiSeq.

[0098] 3) Data analysis

[0099] The raw sequence data obtained by sequencing was first screened for high-quality data by Barcode and primer information, and the screening criteria were as follows: containing complete sequence; having complete primer sequence; removing Barcode, primer, and adapter; sequence length must be greater than 450 bp, not containing ambiguous bases; no more than 4 single-base repeats; and the obtained sequence was respectively corresponding to each sample according to the information. Alignment was performed by database alignment tool, and OTU information table was generated by clustering sequences.

[0100] The generated OTU information table was used to analyze microbial community diversity and abundance indices. Mothur software was used to analyze community diversity (Shannon index, Simpson index), abundance (Chao index, Ace index), depth (Good'scovery index), and similarity curves (Rarefaction curve) using OTUs with a similarity level of 97%. Taxonomic comparisons of gene sequences with sequence data in the GreenGene database were performed to obtain taxonomic information. First, each high-quality sequence was compared with the GreenGene database to find the closest species with a confidence level of 97% or higher. Then, all sequences within each OTU were compared to find the closest species within the same OTU.

[0101] To examine the differences and similarities among the samples at the genus level, the sequencing data of each sample were clustered using the heatmap2 function of the R gplots package. Principal component analysis was performed to examine the similarity between samples. First, a phylogenetic tree containing all sequences was constructed. Then, pairwise distances were calculated for all samples. The phylogenetic tree was pruned to contain only sequences from the two target samples. Principal component analysis was then performed based on the constructed similarity matrix. Decision trees or Random Forest algorithms (Random Forest package in R (http: / / cran.r-project.org / )) were used to classify the samples.

[0102] (3) Results Analysis

[0103] (3.1) Comparison of bacterial community structure

[0104] The abundance of each phylum of the gut microbiota in the centenarian group is shown in Table 4. Among the gut microbiota, the abundance of Bacteroides is greater than that of Firmicutes, and the abundance of Prevotella is low.

[0105] Table 4

[0106]

[0107]

[0108] Changes in the abundance of Bacteroidetes and Firmicutes determine whether a disease manifests: ① A decrease in both Firmicutes and Bacteroidetes leads to a worsening of the disease. ② A decrease in Firmicutes abundance combined with an increase in Bacteroidetes abundance indicates that the body is in an anti-inflammatory state and no symptoms appear. ③ A decrease in Firmicutes abundance combined with unchanged Bacteroidetes abundance combined with an increase in Proteobacteria abundance leads to the appearance of the disease.

[0109] The abundance of each phylum of the gut microbiota in the samples from the chronic diarrhea group is shown in Table 5. It can be seen that the gut microbiota of patients with chronic diarrhea has an imbalanced structure, with excessive abundance of Prevotella and excessive abundance of Bacteroides and Firmicutes.

[0110] Table 5

[0111]

[0112] The abundance of each phylum of the gut microbiota in the neurotic group sample is shown in Table 6. It can be seen that the gut microbiota of the neurotic group is dysregulated, with excessive abundance of Bacteroides and low abundance of Prevotella, indicating poor diversity.

[0113] Table 6

[0114]

[0115] The abundance of each phylum of the gut microbiota in the group that drank alcohol for social purposes is shown in Table 7. It can be seen that the gut microbiota of the group that drank alcohol for social purposes was dysregulated, with low abundance of Bacteroides, high abundance of Prevotella, and low abundance of Firmicutes.

[0116] Table 7

[0117]

[0118] The abundance of each phylum of the gut microbiota in the alcohol-drinking social group is shown in Table 8. It can be seen that the gut microbiota of the constipated group is dysregulated, with low abundance of Bacteroides, high abundance of Prevotella, and low abundance of Firmicutes.

[0119] Table 8

[0120]

[0121] The abundance of each phylum of the gut microbiota in the samples from diabetic patients is shown in Table 9. It can be seen that the gut microbiota of diabetic patients is dysregulated, with Prevotella spp. being too abundant and Bacteroides spp. being too low.

[0122] Table 9

[0123]

[0124] Table 10 shows the abundance of each phylum of the gut microbiota in the samples from patients with hyperlipidemia and hypertension. It can be seen that the gut microbiota of patients with hyperlipidemia and hypertension has an imbalanced structure, poor variety of Firmicutes, and excessive abundance of Megamonas.

[0125] Table 10

[0126]

[0127] The abundance of each phylum of the gut microbiota in the samples from patients with pulmonary nodules is shown in Table 11. It can be seen that the gut microbiota of patients with pulmonary nodules is dysregulated, with Prevotella spp. being too abundant and Bacteroides spp. being too low.

[0128] Table 11

[0129]

[0130] By comparing sub-healthy individuals and those with illnesses, it was found that their gut microbiota structures were abnormal. Except for individuals with hypertension, hyperlipidemia, and neuroticism, the other groups had a common feature: excessive abundance of Prevotella and excessively low abundance of Bacteroides. However, the gut microbiota structure of centenarians was the opposite, with Bacteroides abundance generally exceeding Firmicutes abundance and Prevotella abundance being low.

[0131] Therefore, the abundance of Bacteroides, Prevotella, Firmicutes, and the Bacteroides / Firmite ratio can be used as criteria for judging whether the intestinal flora of the donor is qualified.

[0132] In addition, several representative bacterial groups were found in the above populations, and further cross-sectional comparisons were made among these representative bacterial groups.

[0133] (3.2) Abundance analysis of several representative core bacteria

[0134] The results for the centenarian group are shown in Tables 12-15, where ND represents 0, "↓" indicates a decrease compared to the normal value, and "↑" indicates an increase compared to the normal value.

[0135] Table 12

[0136]

[0137]

[0138] Table 13

[0139] Lactobacillus (%) Eubacterium (%) AKK (%) Oscillospira (%) Megasphaera (%) B001 1.4969 0.3992 ND 0.2785 0.0067 B002 0.2350 0.0202↓ 0.0033 0.0033↓ ND B003 6.3713 0.0170↓ 6.4777 ND 0.1026 B004 0.0469↓ 0.0033↓ ND 0.0033↓ ND B005 0.0462↓ 1.1910 0.4378 0.4377 ND Normal 0.166-6.5835 0.0574-5.8432 0.0018-7.8352 0.0126-0.8558 0-21.672

[0140] Table 14

[0141] Prevotella (%) Bacteroides (%) Bacteroides uniformis (%) Bacteroides vulgatus (%) Bacteroides caccae (%) B001 15.8306 21.5599 0.7004 ND 3.8648 B002 0.0773 60.2271 0.5744 4.689 28.9072↑ B003 8.8733 10.6582 0.0683 0.63 2.5357 B004 2.4550 48.9367 0.3723 14.637 0.0738↓ B005 1.5443 32.1872 0.3914 3.482 13.3864 Normal - - 0.0332-8.3187 0.0008-19.4054 0.2401-24.6276

[0142] Table 15

[0143] Bacteroides ovatus (%) Bacteroides massiliensis (%) Bacteroides stercoris (%) Bacteroides dorei (%) Bacteroides fragilis (%) B001 0.0602 ND 0.769 ND ND B002 1.2718 ND 13.527 0.889 1.447 B003 0.0136 ND ND 1.126 ND B004 1.2057 ND 3.661 8.144 2.939 B005 0.1177 1.246 2.046 ND 1.848 Normal 0.0005-1.4154 0-7.7682 0.0250-10.6608 0-0.0081 0-3.564

[0144] Results for the perennial diarrhea population are shown in Tables 16-19.

[0145] Table 16

[0146] Number Eubacterium eligens (%) Bifidobacterium adolescentis (%) Bifidobacterium longum (%) Faecalibacterium (%) Roseburia (%) H001 0.0901↓ ND ND 0.5003↓ 0.2902↓ H002 0.2633↓ 0.1267 ND 1.9067↓ 0.0333↓ H003 0.1267↓ 0.040 0.020 1.0134↓ 1.0967↓ Normal 0.3366-3.237 0.0425-7.636 0-0.0996 5.1543-18.1656 2.0832-31.45

[0147] Table 17

[0148] Number Lactobacillus (%) Eubacterium (%) AKK (%) Oscillospira (%) Megasphaera (%) H001 0.1401↓ 0.0934 ND 0.1167 8.7853 H002 0.0567↓ 0.1433 ND 0.5000 ND H003 0.0567↓ 0.1867 ND 0.1967 0.0033 Normal 0.166-6.5835 0.0574-5.8432 0.0018-7.8352 0.0126-0.8558 0-21.672

[0149] Table 18

[0150]

[0151]

[0152] Table 19

[0153] Number Bacteroides ovatus (%) Bacteroides massiliensis (%) Bacteroides dorei (%) Bacteroides fragilis (%) H001 0.0367 ND ND ND H002 0.2200 1.533 0.310 0.620 H003 0.0167 2.220 ND ND Normal 0.0005-1.4154 0.0250-10.6608 0-0.0081 0-3.564

[0154] The results for the neurotic group are shown in Tables 20-23.

[0155] Table 20

[0156] Number Eubacterium eligens (%) Bifidobacterium adolescentis (%) Bifidobacterium longum (%) Faecalibacterium (%) Roseburia (%) H004 0.7106 ND 0.0269 14.4922 0.3840↓ H005 1.0259 ND 0.027 6.5671 ND Normal 0.3366-3.237 0.0425-7.636 0-0.0996 5.1543-18.1656 2.0832-31.45

[0157] Table 21

[0158]

[0159] Table 22

[0160] Number Lactobacillus (%) Eubacterium (%) AKK (%) Oscillospira (%) Megasphaera (%) H004 0.4371↓ 39.3879 0.6935 0.260 0.0166↓ H005 0.0502↓ 62.5350↑ 0.1749 ND 47.7827↑ ND - - 0.0332-8.3187 0.0008-19.4054 0.2401-24.6276

[0161] Table 23

[0162] Normal Number Prevotella (%) Bacteroides (%) Bacteroides uniformis (%) H004 7.3000 11.492 Bacteroides vulgatus (%) 5.492 H005 0.0636 Bacteroides caccae (%) 0.721 ND Normal 0.0005-1.4154 0-7.7682 0-0.0167 0-7.2435

[0163] The results for groups of people who drink alcohol for social occasions are shown in Tables 24-27.

[0164] Table 24

[0165] Number Bacteroides ovatus (%) Bacteroides massiliensis (%) Bacteroides dorei (%) Bacteroides fragilis (%) ND B006 0.1261↓ ND ND 4.6138↓ 1.8493↓ B007 0.3035↓ ND ND 2.5817↓ 0.0067↓ B008 0.3555 0.0273↓ 0.021↓ 4.6520↓ 0.0580↓ B009 0.8016 Normal 0.027↓ 2.5543↓ 0.7750↓ Number 0.3366-3.237 0.0425-7.636 0-0.0996 5.1543-18.1656 2.0832-31.45

[0166] Table 25

[0167] Eubacterium eligens (%) Bifidobacterium adolescentis (%) Bifidobacterium longum (%) Faecalibacterium (%) Roseburia (%) Normal B006 0.9237 0.1394 Number 0.1932 Lactobacillus (%) B007 0.5346 0.1748 0.1183 0.6493 Eubacterium (%) B008 0.4380 0.9304 0.3216 0.3216 2.8449 B009 0.0473↓ 0.3109 AKK (%) 0.2773 11.9642 Oscillospira (%) 0.166-6.5835 0.0574-5.8432 0.0018-7.8352 0.0126-0.8558 0-0.0999

[0168] Table 26

[0169] Megasphaera (%) ND ND Normal Number Prevotella (%) B006 47.5221↑ 8.9401↓ 0.0466 2.978 0.9804 B007 47.6671↑ 0.1925 0.7877 1.547 1.7573 B008 48.6596↑ 22.0009↓ 1.0021 Bacteroides (%) 2.4340 B009 44.5134↑ 17.6425↓ 0.1279 Bacteroides uniformis (%) 1.0263 Bacteroides vulgatus (%) - - 0.0332-8.3187 0.0008-19.4054 0.2401-24.6276

[0170] Table 27

[0171] Bacteroides caccae (%) Normal Number Bacteroides ovatus (%) Bacteroides massiliensis (%) B006 0.0399 Bacteroides dorei (%) 3.341 Bacteroides fragilis (%) B007 0.2333 0.559 2.481 0.548 B008 0.0204 Normal 3.515 Number B009 0.4872 Eubacterium eligens (%) 5.644 1.310 Bifidobacterium adolescentis (%) 0.0005-1.4154 0-0.0081 0.0250-10.6608 0-3.564

[0172] The results for the constipation group are shown in Tables 28-31.

[0173] Table 28

[0174] Bifidobacterium longum (%) Faecalibacterium (%) Roseburia (%) ND ND ND B0010 0.0858↓ 0.0429 0.6996 1.5972↓ 0.1188↓ Normal 0.3366-3.237 0.0425-7.636 0-0.0996 5.1543-18.1656 2.0832-31.45

[0175] Table 29

[0176] Number Lactobacillus (%) Eubacterium (%) AKK (%) Oscillospira (%) Megasphaera (%) B0010 1.1484 0.5082 0.0033 ND ND Normal 0.166-6.5835 0.0574-5.8432 0.0018-7.8352 0.0126-0.8558 0-21.672

[0177] Table 30

[0178] Number Prevotella (%) Bacteroides (%) Bacteroides uniformis (%) Bacteroides vulgatus (%) Bacteroides caccae (%) B0010 54.5325↑ 10.5996↓ 0.3861 3.967 2.2407 Normal - - 0.0332-8.3187 0.0008-19.4054 0.2401-24.6276

[0179] Table 31

[0180] Number Bacteroides ovatus (%) Bacteroides massiliensis (%) Bacteroides dorei (%) Bacteroides fragilis (%) B0010 0.3861 0.769 1.261 0.858 Normal 0.0005-1.4154 0.0250-10.6608 0-0.0081 0-3.564

[0181] The results for the diabetes group are shown in Tables 32-35.

[0182] Table 32

[0183] Number Eubacterium eligens (%) Bifidobacterium adolescentis (%) Bifidobacterium longum (%) Faecalibacterium (%) Roseburia (%) B0011 0.2567↓ ND 0.007 9.6303 4.1368 B0012 0.1467↓ ND ND 4.9812↓ 1.1169↓ Normal 0.3366-3.237 0.0425-7.636 0-0.0996 5.1543-18.1656 2.0832-31.45

[0184] Table 33

[0185] Number Lactobacillus (%) Eubacterium (%) AKK (%) Oscillospiraceae (%) Megasphaera (%) B0011 4.4401 0.1900 ND 0.5500 0.0167 B0012 1.1736 0.7869 0.0133 0.2467 2.3739 Normal value 0.166-6.5835 0.0574-5.8432 0.0018-7.8352 0.0126-0.8558 0-21.672

[0186] Table 34

[0187] Number Prevotella (%) Bacteroides (%) Bacteroides uniformis (%) Bacteroides vulgatus (%) B0011 44.3348↑ 10.7004 1.3167 ND B0012 55.9864↑ 15.5903 0.8069 6.828％ Normal value - - 0.0332-8.3187 0.0008-19.4054

[0188] Table 35

[0189] Number Bacteroides (%) Bacteroides ovatus (%) Bacteroides caccae (%) Bacteroides stercoris (%) B0011 4.6268 0.5200 ND 2.177 B0012 1.6871 0.1167 0.680 3.427 Normal value 0.2401-24.6276 0.0005-1.4154 0-4.0557 0.0250-10.6608

[0190] The results for the hypertension and hyperlipidemia patients are shown in Tables 36-39.

[0191] Table 36

[0192] Number Eubacterium (%) Bifidobacterium adolescentis (%) Bifidobacterium longum (%) Faecalibacterium (%) Rothia (%) B0013 0.1746↓ ND 0.010↓ 4.6781↓ 8.0430 Normal value 0.3366-3.237 0.0425-7.636 0-0.0996 5.1543-18.1656 2.0832-31.45

[0193] Table 37

[0194] Number Lactobacillus (%) Akkermansia (%) AKK (%) Oscillospiraceae (%) Megasphaera (%) B0013 1.4343 0.2924 ND 0.3600 16.0834 Normal value 0.166-6.5835 0.0574-5.8432 0.0018-7.8352 0.0126-0.8558 0-21.672

[0195] Table 38

[0196] Number Prevotella (%) Bacteroides (%) Bacteroides uniformis (%) Bacteroides vulgatus (%) Bacteroides (%) B0013 0.2889↓ 40.5510 0.6391 3.388 5.0632 Normal value - - 0.0332-8.3187 0.0008-19.4054 0.2401-24.6276

[0197] Table 39

[0198] Number Bacteroides ovatus (%) Bacteroides massiliensis (%) Bacteroides caccae (%) Bacteroides stercoris (%) B0013 0.5147 4.898 14.220 6.760 Normal value 0.0005-1.4154 0-7.7682 0-4.0557 0.0250-10.6608

[0199] The results for the pulmonary nodule patient group are shown in Tables 40-43.

[0200] Table 40

[0201]

[0202]

[0203] Table 41

[0204] Number Lactobacillus (%) Akkermansia (%) AKK (%) Oscillospiraceae (%) Megasphaera (%) B0014 0.4517 1.1260 0.2813 0.5753 ND Normal value 0.166-6.5835 0.0574-5.8432 0.0018-7.8352 0.0126-0.8558 0-21.672

[0205] Table 42

[0206] Number Prevotella (%) Bacteroides (%) Bacteroides uniformis (%) Bacteroides vulgatus (%) Bacteroides (%) B0014 38.7335↑ 8.6373↓ 0.1233 ND 0.4886 Normal value - - 0.0332-8.3187 0.0008-19.4054 0.2401-24.6276

[0207] Table 43

[0208] Number Bacteroides ovatus (%) Bacteroides massiliensis (%) Bacteroides stercoris (%) Bacteroides caccae (%) Escherichia (%) B0014 0.0266 2.109％ 1.617 0.572 0.0066 Normal value 0.0005-1.4154 0-7.7682 0.0250-10.6608 0-3.564 0-9.3023

[0209] Based on the aforementioned characteristic bacterial flora, the abundance of *Femobacterium* is generally low in the gut microbiota of centenarians, those in a sub-healthy state, and patients with diseases. Other bacterial genera, such as *Eubacterium mucinum*, *Bifidobacterium adolescentis*, *Bifidobacterium longum*, *Rhodotorula spp.*, *Lactobacillus*, *Eubacterium*, AKK, and *Oscillatoria*, are more or less abnormal in the gut microbiota of sub-healthy individuals, those with diseases, and centenarians. The Bacteroidetes flora of centenarians includes *Bacteroides monomorpha*, *Bacteroides vulgaris*, *Bacteroides var. ovata*, *Bacteroides masei*, *Bacteroides fecalis*, *Bacteroides humaneum*, and *Bacteroides fecalis*. Therefore, it can be preliminarily considered that a qualified donor should have: a normal to high abundance of *Femobacterium*, and core bacterial genera *Eubacterium mucinum*, *Bifidobacterium adolescentis*, *Bifidobacterium longum*, *Rhodotorula spp.*, *Lactobacillus*, *Eubacterium*, AKK, and *Oscillatoria* should be within the normal range as much as possible. An occasional abnormality of 2-3 genera in a single test does not affect the results, but several abnormal stool tests indicate that the donor may not be a qualified donor. The Bacteroidetes flora of centenarians suggests that a qualified donor gut microbiota must contain abundant *Bacteroides monomorpha*, *Bacteroides vulgaris*, *Bacteroides var. ova*, and *Bacteroides ova*. A low abundance of these species indicates an unqualified donor. *Bacteroides masei*, *Bacteroides fecalis*, *Bacteroides human fecalis*, and *Bacteroides fecalis* are also important components of a qualified Bacteroidetes genus, and their abundance and diversity reflect the quality of the donor gut microbiota.

[0210] (3.2) Metabolite Analysis

[0211] The analysis method was (commissioned to Hangzhou Guhe Information Technology Co., Ltd. for testing):

[0212] ① Collecting feces: Obtaining samples from the human intestines.

[0213] ② Extraction of metabolites: Separating metabolites from the matrix in the sample.

[0214] ③ Metabolite analysis: Mass spectrometry was used to analyze the mass and structural information of metabolites.

[0215] ④ Metabolite identification: Compare the mass spectra of metabolites with databases of known standard substances (NIST, KEGG). Regarding metabolite scoring, establish a predictive model for metabolite abundance based on microbial community characteristics, and convert the predicted values ​​into values ​​at the population distribution level.

[0216] The results of metabolites in fecal samples from centenarians are shown in Figure 44. "↓" indicates a decrease compared to the normal value, and "↑" indicates an increase compared to the normal value.

[0217] Table 44

[0218]

[0219]

[0220] The results of metabolites in fecal samples from the chronic diarrhea group are shown in Figure 45.

[0221] Table 45

[0222]

[0223] The results of metabolites in the fecal samples of the neurotic group are shown in Figure 46.

[0224] Table 46

[0225]

[0226] The results of metabolites in fecal samples from the group that drank alcohol for social occasions are shown in Figure 47.

[0227] Table 47

[0228]

[0229] The results of metabolites in the fecal samples of the constipation group are shown in Figure 48.

[0230] Table 48

[0231]

[0232] The results of metabolites in fecal samples from the diabetic group are shown in Figure 49.

[0233] Table 49

[0234]

[0235]

[0236] The results of metabolites in fecal samples from the hypertension and hyperlipidemia group are shown in Figure 50.

[0237] Table 50

[0238]

[0239] The results of metabolites in fecal samples from the hypertension and hyperlipidemia group are shown in Figure 51.

[0240] Table 51

[0241]

[0242] From the perspective of gut microbiota metabolites in centenarians, sub-healthy individuals, and patients with diseases, short-chain fatty acids, tryptophan, indole, and 5-HT showed the most abnormalities, followed by histamine, hydrogen sulfide, NO, γ-aminobutyric acid, and DOPA, and finally secondary bile acids, p-cresol, and LPS.

[0243] 3.3. Using metagenomics analysis, further understand the long-term stability and metabolism of the gut microbiota in qualified donors.

[0244] Based on the above analysis results and the corresponding indicators mined out, a donor screening method was designed. 16S rRNA sequencing and mass spectrometry were performed on the samples to establish an evaluation system for qualified donors: (1) Firmicutes: Bacteroidetes (F / B): 0.6-1.5; (2) Enterotype is Bacteroidetes; (3) Prevotella abundance <10%; (4) Bacteroidetes genus includes: Bacteroidetes polymorpha, Bacteroidetes monomorpha, Bacteroidetes vulgaris, Bacteroidetes ovalis, and Bacteroidetes fecalis; (5) Must have normal microbial metabolites: short-chain fatty acids, tryptophan, indole, 5-HT, LPS. Thirteen qualified volunteers were selected from 72 volunteers, and six volunteers who could cooperate with the study for at least one year were selected. Multi-omics (metagenomics + metabolomics) was used to study the microbial community, metabolic function, and immune regulation capacity of the donors selected in the first phase for 6 months to understand the donor resistance, stability, and resilience. The information of the enrolled donors is shown in Table 52.

[0245] Table 52

[0246]

[0247]

[0248] The gut microbiota structure of donor 16S rRNA, used for clinical validation of efficacy and safety, is as follows: Figures 1-6 As shown in Tables 53 and 54, the abundance (%) of the core microbial community of the selected donors is as follows.

[0249] Table 53

[0250]

[0251] Table 54

[0252]

[0253]

[0254] The results of metabolite analysis are shown in Table 55.

[0255] Table 55

[0256]

[0257] The timing of donor fecal collection is shown in Table 56.

[0258] Table 56

[0259]

[0260] Feces from donors were collected for six consecutive months or more throughout the year, and metagenomic sequencing was performed to understand the composition of the gut microbiota, enabling continuous analysis of donors throughout the year.

[0261] The results are as follows Figures 7-12 As shown, there were significant differences in the microbial community structure among donors. However, a continuous comparison of the donor's own microbial community throughout the year showed no significant changes in its overall structure. Figure 13 This indicates that the donor microbiota composition can maintain long-term stability, ensuring the effectiveness of treatment.

[0262] Example 2

[0263] This embodiment utilizes the qualified donors selected in Example 1 for actual clinical application.

[0264] The fecal microbiota of donors (S001, S006, S010, S011, S018) were made into capsules and used in clinical validation projects to: ① verify the compatibility, efficacy, safety and effectiveness of the donor capsules; ② analyze the microbiota of donors and recipients before and after treatment, and determine the appropriate FMT transplantation method and dosage from six dimensions: intestinal microbial richness, microbiota structure, progressive microbiota, stable microbiota, beneficial pathways and harmful pathways, and find a precise intestinal microbiota transplantation model for donors and recipients.

[0265] Fecal samples were collected from patients before and after treatment. Fecal samples from corresponding batches of donors were also retained to compare the changes and correlations between patient and donor microbiota. Recipient selection criteria: advanced, metastatic, or recurrent lung adenocarcinoma (stage IIIB / IIIC / IV), PD-L1 expression of 0, and adequate organ function. These three conditions are essential and must be met simultaneously. Selected recipients were numbered, and donor-recipient matching is shown in Table 57.

[0266] Table 57

[0267] Recipient number Donor number R001 S018 R002 S011 R003 S018 R004 S010 R005 S006 R006 S010 R007 S001 R008 S001 R009 S006 R010 S006 R011 S006

[0268] Research drugs / technologies, specifications, and administration methods:

[0269] PD-1 antibody

[0270] Specification: 100mg: 10mL;

[0271] Administration: Weight < 60 kg: 3 mg / kg IV D1 Q3W; Weight ≥ 60 kg: 200 mg IV D1 Q3W;

[0272] Cisplatin

[0273] Specifications: 50mg: 50mL;

[0274] Administration method: 75 mg / m² 2 IV D1 Q3W;

[0275] Pemetrexed

[0276] Specifications: 100mg / vial, 500mg / vial;

[0277] Administration method: 500 mg / m² 2 IV D1 Q3W;

[0278] Note: IV: Intravenous administration; D1: Administration time is the first day of the treatment cycle; Q3W: The treatment cycle is repeated every 3 weeks (i.e., 21 days).

[0279] Microbial transplantation

[0280] Specifications: 1g of bacterial flora per capsule.

[0281] FMT (Factory Microbiota Transplantation) Method: Take 2-3 capsules orally every 5 minutes. A total of 40 capsules are required for one transplant.

[0282] Sixteen patients with lung adenocarcinoma were evaluated, and 14 met the inclusion criteria (Table 58). All 14 patients underwent FMT transplantation, and 11 of them completed the efficacy evaluation of chemotherapy + immunotherapy + FMT treatment.

[0283] Table 58

[0284]

[0285]

[0286] Research process as follows Figure 14 As shown, it specifically includes:

[0287] (1) Subjects first received a course of PD-1 antibody combined with cisplatin and pemetrexed.

[0288] (2) One course of intestinal microbiota transplantation (FMT) was performed one week before the start of the second treatment course (3 times, transplanted every other day, 40g of microbiota transplanted each time).

[0289] (3) One course of intestinal microbiota transplantation (FMT) was performed one week before the start of the third treatment course (1 time, 40g of transplanted microbiota).

[0290] (4) Perform one course of intestinal microbiota transplantation (FMT) one week before the start of the fourth treatment course (1 time, 40g of transplanted microbiota).

[0291] (5) After microbiota transplantation, PD-1 antibody combined with cisplatin and pemetrexed induction therapy is completed, and PD-1 antibody combined with pemetrexed or PD-1 antibody monotherapy is used for maintenance therapy until disease progression, intolerable toxicity occurs, new anti-tumor therapy is received, informed consent is withdrawn, loss to follow-up or death occurs, or other situations in which the investigator determines that treatment should be stopped, whichever occurs first.

[0292] Fecal collection:

[0293] 1) Donor fecal study site: Fecal samples retained from the batch of donors corresponding to the recipient's fecal transplant capsule.

[0294] 2) Recipient fecal collection study site:

[0295] i. Before the subject received the first PD-1 antibody combined with cisplatin and pemetrexed treatment;

[0296] ii. Before the subject receives the first FMT treatment;

[0297] iii. Before the subject receives the second FMT treatment;

[0298] iv. Before the subject received the third FMT treatment;

[0299] v. One month after the subject received the third FMT treatment;

[0300] vi. Three months after the subject received the third FMT treatment;

[0301] vii. Six months after the subject received the third FMT treatment;

[0302] viii. Nine months after the subject received the third FMT treatment;

[0303] ix. Twelve months after the subject received the third FMT treatment;

[0304] x. Eighteen months after the subject received the third FMT treatment.

[0305] A diagram illustrating the total treatment course is shown below. Figure 15As shown in Table 59, the treatment efficacy results were as follows: 1 of the 11 patients who received chemotherapy + immunotherapy + FMT was cured; 6 patients showed partial remission based on imaging and clinical symptoms; and 4 patients showed no disease progression based on imaging, tumor markers, and other clinical tests. No cases of disease progression were observed among the 11 enrolled patients, indicating that the three-stage FMT transplantation is effective, resulting in stable, partially remitted, or even cured patients (ORR = 64%), median follow-up time 24.8 months, median OS = 15.6 months, median PFS = 11.9 months, 1-year OS rate 77.1%, 2-year OS rate 46.3%, 1-year PFS rate 48%, 2-year PFS rate 48%, mean DOR = 14.2 months.

[0306] Table 59

[0307]

[0308] The safety results of the treatment are shown in Table 60. Four patients died, all of which were unrelated to the study drug (two deaths were thought to be caused by PD and two deaths were thought to be caused by infection). No patients experienced drug-related grade 3 or higher adverse events or SAEs.

[0309] Table 60

[0310]

[0311]

[0312] As shown in Table 61, the results of the lung adenocarcinoma treatment program of this invention are superior to those of similar studies in terms of one-year and two-year overall survival (OS), objective response rate (ORR), progression-free survival (PFS), complete response rate (CR), and partial response rate (PR).

[0313] Table 61

[0314]

[0315] The following are some examples of treatment cases.

[0316] Case 1:

[0317] A 68-year-old male patient with advanced lung cancer was enrolled in the study. He was PDL-1 negative. A chest CT scan on April 7, 2022, showed a tumor lesion of 28*26mm in the right upper lung. After treatment with the FMT combined with PD-1 monoclonal antibody of this invention, a follow-up chest CT scan on June 24, 2022, showed the tumor lesion had shrunk to 8*8mm. A follow-up chest CT scan on September 22, 2023, showed the tumor lesion had shrunk to 2-3mm. Figure 16Serum tumor biomarker testing showed that in April 2022, carcinoembryonic antigen (CEA) was 63.67 ng / mL and (CYFRA211) was 3.35 ng / mL. After treatment with FMT combined with PD-1 monoclonal antibody of the present invention, the serum tumor biomarker was retested in August 2022, and the carcinoembryonic antigen (CEA) was 1.37 ng / mL, returning to normal.

[0318] Case 2:

[0319] The patient was a 69-year-old male with poorly differentiated adenocarcinoma of lung cancer, with metastases to the left hilar lymph nodes and the right femoral shaft. He was PD / L-1 negative. A chest CT scan on August 23, 2022, showed a tumor lesion of 60*45mm in the right upper lung. After treatment with FMT combined with PD-1 monoclonal antibody, a follow-up chest CT scan on December 20, 2022, showed the tumor lesion had shrunk to approximately 33mm in diameter. A follow-up chest CT scan on September 8, 2023, showed the tumor lesion had shrunk to no more than 5mm in diameter. Figure 17 Serum tumor biomarker detection: In August 2022, the cytokeratin fragment CYFRA211 was 7.53 ng / mL. After treatment with the FMT combined with PD-1 monoclonal antibody of the present invention, the serum tumor biomarker was retested in October 2023, and the cytokeratin fragment CYFRA211 was 2.42 ng / mL, returning to normal.

[0320] Case 3:

[0321] Male, 73 years old, lung adenocarcinoma with adrenal metastasis (cTxN0M1, stage IV); no gene mutations detected, PD / L-1 negative expression. A chest CT scan in February 2024 showed a tumor lesion of 64*44mm in the right upper lung. After treatment with the FMT combined with PD-1 monoclonal antibody of this invention, a follow-up chest CT scan in May 2024 showed the tumor lesion had shrunk to 28*15mm. A follow-up chest CT scan in July 2024 showed the tumor lesion maintained at 28*15mm. Figure 18 ).

[0322] Results of metagenomic analysis of donor and recipient gut microbiota: Figures 19-28 As shown, after gut microbiota transplantation, the gut microbiota structure of most patients changed significantly, indicating that gut microbiota transplantation can improve the community structure of the recipient's gut microbiota.

[0323] Figure 29The diagram shows the distribution of β-diversity in the recipient's gut microbiota structure after microbiota transplantation. Figure A is a box plot of β-diversity, with the vertical axis representing distance values ​​(0-1): the smaller the distance value, the more similar the microbiota structure; the larger the distance value, the greater the microbiota difference. Figure B is a network diagram of β-diversity, where each node represents a different sample, and the lines represent the microbiota similarity. The thicker / shorter the lines, the more similar the microbiota structure; no lines indicate large differences. Key clusters: "small groups": donors S001, S006, and S010 are closely connected with patients L11, L14, L12, etc., indicating that the microbiota of S001, S006, and S010 have successfully colonized these donors, forming a typical microbiota lineage.

[0324] In summary, after microbiota transplantation, the patient's microbiota is influenced by both their original microbiota and the donor's. A typical post-transplant microbiota profile is formed centered around donor S001 / 006 / 010.

[0325] Figure 30 The graphs show the colonization efficiency of bacterial communities at the strain level. Graph A is a frequency distribution histogram, with the horizontal axis representing the genetic distance between strains (a larger value indicates more significant evolutionary divergence) and the vertical axis representing frequency (the frequency of strains at a specific genetic distance). The highest frequency is found at a genetic distance ≈ 0.01, indicating that colonization is most likely when the genetic distance between strains is ≤ 0.01, with a total of 93 strains. Graph B is a scatter plot, with the horizontal axis representing persistence and the vertical axis representing FMT (free-to-molecular transplantation) rate. The upper right region shows high-frequency strains with high FMT rates (93 strains, corresponding to Graph A); the lower left region shows strains with low persistence and low FMT rates (60 strains). Graph C is a stacked bar chart, showing several families that colonize well and poorly.

[0326] Figure 31 This image shows the results of the recipient's gut microbiota structure before and after microbiota transplantation. After microbiota transplantation, most patients experienced significant changes in their gut microbiota, and these changes could persist for a period of time after the transplantation.

[0327] Analyze the colonization scale of the donor strain and the composition of the recipient microbial community. Figure 32A For the β-diversity box plot, the vertical axis represents distance values ​​(0-1): the smaller the distance value, the more similar the bacterial community structure; the larger the distance value, the greater the difference between the bacterial communities. Figure 32B The plot is a scatter plot. The horizontal axis represents the number of donor strains successfully colonized, and the vertical axis represents the number of residual strains in the recipient. From the overall distribution of the points, most of the points are located in the lower left corner to the right of the middle, indicating that the success rate of FMT strain transplantation is about 70%. Figure 32C The graph is a stacked bar chart. The vertical axis represents the patient sample ID, blue represents the donor, and purple represents the recipient. By comparing the stacked bar charts of donors and recipients, we can intuitively see the number of strains from the donor and the number of strains originally present in each recipient. We can see that the blue portion of most recipients is relatively large, indicating that FMT has a higher success rate.Figure 32D As a strain sharing network, each node represents a different strain unit (OTU / ASV). The strains form multiple relatively independent clusters, and the connections between strains within each cluster are relatively dense (connections: representing the co-occurrence relationship between strains in different individuals, i.e., two strains appearing simultaneously in multiple samples), indicating that they often co-occur in different individuals. This suggests that there is a certain synergistic relationship between the strains or a preference for similar environments.

[0328] At the bacterial strain level, both donors and post-transplant patients maintain long-term stability, but individual differences are very high. The success rate of FMT at the bacterial strain level is around 70%, but individual variations exist.

[0329] Comprehensive metagenomic analysis results show that in the intestinal microbiota transplantation study of this invention, the donors maintained stable long-term Shannon diversity and a stable microbiota structure, resulting in high colonization of the donor microbiota in the patients after transplantation. Therefore, based on metagenomic data, clinical efficacy, laboratory test data, and imaging results, it is currently feasible to use donors selected using the screening method designed in this invention for patients with advanced lung adenocarcinoma.

[0330] In summary, this invention conducts an in-depth analysis of the human gut microbiota, analyzes the relationship between various related indicators and the effect of gut microbiota transplantation, conducts sufficient clinical verification, designs a method for screening gut microbiota transplantation donors, and establishes a standard for judging whether a gut microbiota transplantation donor is qualified, specifically including: (1) Firmicutes: Bacteroidetes (F / B): 0.6-1.5; (2) Enterotype is Bacteroidetes; (3) Prevotella abundance <10%; (4) Bacteroidetes genus includes: Bacteroidetes polymorpha, Bacteroidetes monomorpha, Bacteroidetes vulgaris, Bacteroidetes ovalis, and Bacteroidetes fecalis; (5) Must have normal gut microbiota metabolites: short-chain fatty acids, tryptophan, indole, 5-HT, LPS; may have some abnormal gut microbiota metabolites: histamine, hydrogen sulfide, NO, γ-aminobutyric acid, DOPA, secondary bile acids, p-cresol. This provides a new method and new ideas for efficient and accurate screening of gut microbiota transplantation donors.

[0331] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A biomarker for screening intestinal flora transplant donors, characterized in that, The biomarkers include gut microbiota and their metabolites. The gut microbiota includes Firmicutes and Bacteroidetes. The Bacteroidetes include Bacteroides and Prevotella. The Bacteroides include Bacteroides pleomorpha, Bacteroides monomorpha, Bacteroides vulgaris, Bacteroides ovalis, and Bacteroides fecalis. The metabolites include short-chain fatty acids, tryptophan, indole, 5-hydroxytryptamine, and lipopolysaccharides; The short-chain fatty acids include acetic acid, propionic acid, and butyric acid.

2. The use of the biomarker and / or its detection reagent for screening intestinal flora transplantation donors as described in claim 1 in the preparation of products for screening intestinal flora transplantation donors.

3. A kit for screening intestinal flora transplant donors, characterized in that, The kit includes reagents for detecting the biomarkers for screening intestinal flora transplant donors as described in claim 1; Preferably, the reagents include reagents for detecting the abundance of the gut microbiota and its metabolites, including 16S rRNA gene sequencing reagents.

4. The use of the biomarkers and / or detection reagents for screening intestinal flora transplantation donors as described in claim 1 in the screening of intestinal flora transplantation donors.

5. The application according to claim 4, characterized in that, The applications include screening donors for gut microbiota transplantation to treat lung adenocarcinoma.

6. A method for screening intestinal flora transplantation donors, characterized in that, The method for screening intestinal flora transplantation donors includes: The abundance of the biomarker for screening intestinal flora transplantation donors as described in claim 1 is detected in the feces of the donor to be screened; the donor is then determined to be a qualified donor based on the detection results.

7. The method for screening intestinal flora transplantation donors according to claim 6, characterized in that, The criteria for determining whether someone is a qualified donor include: (1) Abundance of Firmicutes: The ratio of abundance of Bacteroidetes was 0.6 to 1.5; (2) The enteric type is Bacteroidetes type; (3) Abundance of Prevostella spp. <10%; (4) The genus Bacteroides contains Bacteroides polymorpha, Bacteroides monomorpha, Bacteroides vulgaris, Bacteroides succinate, Bacteroides ovalis and Bacteroides fecalis; (5) The abundance of acetic acid is 15-95, the abundance of propionic acid is 15-90, the abundance of butyric acid is 15-95, the abundance of tryptophan is 15-95, the abundance of indole is 15-90, the abundance of 5-hydroxytryptamine is 15-95 and the abundance of lipopolysaccharide is 0-85.

8. A model for screening intestinal flora transplantation donors, characterized in that, The model is used to perform the steps in the method for screening intestinal flora transplantation donors as described in claim 6 or 7, and the model includes a detection unit and a judgment unit. The detection unit is used to perform the following: acquiring the abundance of the biomarker for screening intestinal flora transplantation donors as described in claim 1 in the feces of the donor to be screened; The judgment unit is used to perform the following: calculation and judgment based on the result of the detection unit; The criteria for determining whether someone is a qualified donor include: (1) Abundance of Firmicutes: The ratio of abundance of Bacteroidetes was 0.6 to 1.5; (2) The enteric type is Bacteroidetes type; (3) Abundance of Prevostella spp. <10%; (4) The genus Bacteroides contains Bacteroides polymorpha, Bacteroides monomorpha, Bacteroides vulgaris, Bacteroides succinate, Bacteroides ovalis and Bacteroides fecalis; (5) The abundance of acetic acid is 15-95, the abundance of propionic acid is 15-90, the abundance of butyric acid is 15-95, the abundance of tryptophan is 15-95, the abundance of indole is 15-90, the abundance of 5-hydroxytryptamine is 15-95 and the abundance of lipopolysaccharide is 0-85.

9. An electronic device comprising one or more processors and a memory for storing executable instructions, characterized in that, The one or more processors are configured to invoke executable instructions stored in the memory to implement the function of the model for screening intestinal flora transplant donors as described in claim 8.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they perform the function of the model for screening intestinal flora transplantation donors as described in claim 8.

Images