KR20180075401A - Method for diagnosis of bladder cancer using analysis of microbial metagenome - Google Patents

Abstract

The present invention relates to a method for diagnosing bladder cancer through microbial metagenome analysis, and more specifically, to a method for diagnosing bladder cancer by performing analysis on microbial metagenome of bacteria, archaebacteria and the like using samples derived from a subject to analyze increase and decrease of content of extracellular vesicles derived from specific microbe. The extracellular vesicles secreted from microbe of bacteria and the like present in an environment can be absorbed in a body to directly affect occurrence of cancer, and bladder cancer cannot be treated efficiently due to difficulty in early diagnosis before symptoms appear. Thus, by predicting risk of occurrence of bladder cancer in advance through analysis of microbial metagenome of bacteria, archaebacteria and the like using samples derived from a human body according to the present invention, a risk group of bladder cancer can be diagnosed early and predicted to delay a time of occurrence of bladder cancer or prevent occurrence thereof through appropriate management. In addition, bladder cancer can be diagnosed early even after occurrence, thereby reducing incidence of bladder cancer and increasing a therapeutic effect.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for diagnosing bladder cancer,

The present invention relates to a method for diagnosing bladder cancer through microorganism metagenomic analysis, and more particularly, to a method for diagnosing bladder cancer by analyzing a microorganism metagenome such as a bacterium or an Escherichia coli using a sample derived from a subject, And to a method of diagnosing bladder cancer by analyzing the increase and decrease.

Stomach cancer (stomach carcinoma) is a malignant tumor of the bladder. It is composed of 90% of transitional cell carcinoma originating from transitional epithelial cells in direct contact with urine, and other squamous cell carcinoma, adenocarcinoma and sarcoma. Bladder cancer can be classified into superficial bladder cancer confined to bladder mucosa or submucosa, invasive bladder cancer invading muscular layer, and metastatic bladder cancer metastasized to other organs.

It is reported that smoking is the most risk factor for bladder cancer. Smokers are more likely to be bladder cancer than smokers, and bladder is the last place to be discharged through the body. Therefore, carcinogens caused by smoking are the primary risk of bladder cancer. It is known. In addition to smoking, occupational exposure to chemicals is claimed to be a risk. It is known that racial incidence is more than twice as high as that of white or Hispanic races, and the incidence of males is four times higher than that of females. However, in spite of the development of modern medicine, there is no method of predicting bladder cancer by noninvasive method yet, and the conventional diagnosis method is often found when solid cancer such as bladder cancer is advanced. Therefore, , It is an effective method to predict the incidence and causes of bladder cancer in advance and to provide measures to prevent bladder cancer in high-risk groups.

On the other hand, the number of microorganisms that are symbiotic to the human body is 10 times more than that of human cells, and the number of microorganisms is known to be over 100 times that of human genes. Microbiota refers to microbial communities that include bacteria, archaea, and eukarya in a given settlement. Intestinal microbial guns play an important role in human physiology , And it is known to have a great influence on human health and disease through interaction with human cells. Bacteria that coexist in our body secrete nanometer-sized vesicles to exchange information about genes, proteins, etc., into other cells. The mucous membrane forms a physical barrier that can not pass through particles of 200 nanometers (nm) or larger and can not pass through the mucous membrane when the bacteria are symbiotic to the mucous membrane. However, since the bacterial-derived vesicles are usually 100 nanometers or less in size, The mucous membrane is freely absorbed into our bodies.

Metagenomics, also called environmental genomics, is an analysis of metagenomic data from samples taken in the environment. Recently, 16s ribosomal RNA (16s rRNA) base sequence-based method has been able to catalog the bacterial composition of human microbial genome. The 16s rDNA nucleotide sequence of 16s ribosomal RNA can be sequenced by next generation sequencing , NGS) platform (Nature. 2007 Oct 18; 449 (7164): 804-810). However, in the case of bladder cancer, there has been no report on a method of identifying a causative agent of bladder cancer and diagnosing bladder cancer by analyzing a metagenome present in bacterial-derived vesicles in human organs such as blood or urine.

In order to diagnose bladder cancer, the present inventors extracted genes from bacterial and epidermal extracellular vesicles using blood and urine, which are samples derived from a subject, and conducted metagenome analysis thereof. As a result, The present inventors have completed the present invention on the basis thereof.

Accordingly, it is an object of the present invention to provide a method for providing information for diagnosis of bladder cancer through metagenome analysis of extracellular vesicles derived from bacteria and archaea.

It is another object of the present invention to provide a method for diagnosing bladder cancer and a method for predicting the risk of bladder cancer through metagenome analysis of the extracellular vesicles.

However, the technical problem to be solved by the present invention is not limited to the above-mentioned problems, and other matters not mentioned can be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, the present invention provides a method for providing information for diagnosis of bladder cancer, comprising the steps of:

(a) extracting DNA from an extracellular vesicle isolated from a sample of a subject;

(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And

(c) comparing the increase and decrease in the contents of the sample derived from the healthy person with the out-of-cell vesicles derived from the bacteria and the archaeocytes through sequencing analysis of the PCR product.

The present invention also provides a method for diagnosing bladder cancer comprising the steps of:

(a) extracting DNA from an extracellular vesicle isolated from a sample of a subject;

(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And

(c) comparing the increase and decrease in the contents of the sample derived from the healthy person with the out-of-cell vesicles derived from the bacteria and the archaeocytes through sequencing analysis of the PCR product.

The present invention also provides a method for predicting the risk of bladder cancer, comprising the steps of:

(a) extracting DNA from an extracellular vesicle isolated from a sample of a subject;

(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And

(c) comparing the increase and decrease in the contents of the sample derived from the healthy person with the out-of-cell vesicles derived from the bacteria and the archaeocytes through sequencing analysis of the PCR product.

In one embodiment of the present invention, the sample of the sample may be blood or urine.

In another embodiment of the present invention, at least one class selected from the group consisting of Rubrobacteria and Erysipelotrichi isolated from the blood sample of the subject in the step (c) It may be to compare the increase or decrease in the content of the bacterial-derived extracellular vesicles.

In another embodiment of the present invention, the method of the present invention further comprises the steps of: (a) isolating Rubobacterales, Erysipelotrichales, Burkholderiales, Enterobacteriaceae, May be to compare the increase or decrease in the content of one or more order bacterial extracellular vesicles selected from the group consisting of Enterobacteriales, Lactobacillales, Neisseriales, and RF32.

In another embodiment of the present invention, in step (c), there is provided a method of detecting the presence or absence of Oxalobacteraceae, Rikenellaceae, Erysipelotrichaceae, Basil (Bacillaceae), Rhizobiaceae, Fusobacteriaceae, Pseudomonadaceae, Comamonadaceae, Planococcaceae, Enterobacteriaceae, But are not limited to, Enterobacteriaceae, Lachnospiraceae, Corynebacteriaceae, Deinococcaceae, Porphyromonadaceae, Neisseriaceae, Enterobacteriaceae, Derived bacterial-derived extracellular vesicles from the group consisting of Enterococcaceae, Lactobacillaceae, Tissierellaceae, and Peptococcaceae. Of It may be to compare the amount of increase or decrease.

In another embodiment of the present invention, at least one selected from the group consisting of Cupriavidus, Eubacterium, Blautia, and Ketjenbacterium (hereinafter referred to as " Such as Catenibacterium, Collinsella, Geobacillus, Roseburia, Coprococcus, Faecalibacterium, Pseudomonas, Corynebacterium, Mucispirillum, Deinococcus, Anaerococcus, Dorea, Enterococcus, Adlercreutzia, Parabacteroides, Pseudomonas aeruginosa, To compare the increase or decrease in the content of one or more genus bacterial extracellular vesicles selected from the group consisting of Lactobacillus, rc4-4, Peptoniphilus, and Finegoldia .

In another embodiment of the present invention, the method of the present invention may further comprise the steps of: (a) removing cyanobacteria, Gemmatimonadetes, Planctomycetes, Acidobacteria, , And Euryarchaeota. The present invention also provides a method for comparing the content of at least one phylum bacterial-derived extracellular vesicle selected from the group consisting of Euryarchaeota and Euryarchaeota.

In another embodiment of the present invention, in step (c), chloroplast, Methanobacteria, Planctomycetia, Aridobacillerium, Acidobacteriia, and Pedosphaerae. The present invention relates to a method for inhibiting the growth and /

In another embodiment of the present invention, in step (c), Stramenopiles, Streptophyta, Pseudomonadales, Tursiomyces terrales, The amount of one or more order bacterial-derived extracellular vesicles selected from the group consisting of Turicibacterales, Methanobacteriales, Gemmatales, Acidobacteriales, and Ellin329 . ≪ / RTI >

In another embodiment of the present invention, in step (c), the Flavobacteriaceae, Lactobacillaceae, Pseudomonadaceae, The strains of Ruminococcaceae, Comamonadaceae, Turicibacteraceae, Clostridiaceae, Gordoniaceae, Rikenellaceae, 1 > is selected from the group consisting of Prevotellaceae, Methanobacteriaceae, Barnesiellaceae, Peptostreptococcaceae, and Koribacteraceae. To compare the increase or decrease in the content of extracellular vesicles derived from more than one species.

In another embodiment of the present invention, the method of the present invention may further comprise the steps of: (a) isolating Rhizobium, Proteus, Pseudomonas, Lactobacillus, Turicibacter, , Ruminococcus, Klebsiella, Faecalibacterium, Brevundimonas, Clostridium, Jeotgalicoccus, Megasphaera, At least one genus selected from the group consisting of Gordonia, Prevotella, Actinobacillus, Thermoanaerobacterium, and Methanobrevibacter. ) May be to compare the increase or decrease in the content of the extracellular vesicles derived from bacteria.

In another embodiment of the present invention, the blood may be whole blood, serum, plasma, or blood mononuclear cells.

Since the extracellular vesicles released from bacteria and E. coli present in the environment are absorbed into the body to directly affect the cancer development and since it is difficult to diagnose bladder cancer early before symptoms appear, By using meta-genomic analysis of bacterial-derived extracellular vesicles using the derived samples, it is possible to predict the risk of bladder cancer early by diagnosing and predicting the risk group of bladder cancer early, and by appropriately managing it, it is possible to delay the onset period or prevent the onset, Early diagnosis can reduce the incidence of bladder cancer and improve the treatment effect. In addition, metagenomic analysis in patients diagnosed with bladder cancer can avoid the exposure of the causative agent to improve cancer progression or prevent recurrence.

FIG. 1A is a photograph of distribution patterns of bacteria and vesicles by time after oral intestinal bacteria and bacterial-derived vesicles (EV) are administered to a mouse. FIG. 1B is a photograph of blood And various organs were extracted to evaluate the distribution patterns of bacteria and vesicles in the body.
FIG. 2 shows the distribution of bacterial-derived vesicles (EVs) in a bladder cancer patient and a normal blood sample after the bacterial-derived vesicles were separated and the diagnostic performance was significant at the class level by performing a metagenome analysis.
FIG. 3 shows the distribution of bacterial-derived vesicles (EVs) in the bladder cancer patient and the normal blood after the bacterial-derived vesicles were separated and the diagnostic performance was significant at the order level by performing the metagenome analysis.
FIG. 4 shows the distribution of bacterial-derived vesicles (EVs) in the bladder cancer patient and the normal blood after the bacterial-derived vesicles were separated and the diagnostic performance was significant at the family level by performing the metagenome analysis.
FIG. 5 shows the distribution of bacterial-derived vesicles (EVs) in a bladder cancer patient and normal blood after performing bacterial-derived vesicle separation and performing a meta genome analysis and showing a significant diagnostic performance at the genus level.
Figure 6 shows the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at the phylum level by performing a metagenome analysis after separating bacterial-derived vesicles from bladder cancer patients and normal urine.
FIG. 7 shows the distribution of bacterial-derived vesicles (EVs) in a bladder cancer patient and normal urine after bacterial-derived vesicles were separated from the normal urine, and the diagnostic performance was significant at the class level by performing the metagenome analysis.
FIG. 8 shows the distribution of bacterial-derived vesicles (EVs) in the bladder cancer patients and the normal urine after bacterial-derived vesicles were separated from the normal urine, and the metabolic genome analysis was performed to determine the diagnostic performance at the order level.
FIG. 9 shows the distribution of bacterial-derived vesicles (EVs) in a bladder cancer patient and normal urine after bacterial-derived vesicles were separated from the normal urine by performing a meta genome analysis and having diagnostic performance at a family level.
FIG. 10 shows the distribution of bacterial-derived vesicles (EVs) in the bladder cancer patients and the normal urine after bacterial-derived vesicles were separated and the diagnostic performance was significant at the genus level by performing the metagenome analysis.

The present invention relates to a method for diagnosing bladder cancer through analysis of bacterial and archaeal metagenomes. The present inventors extracted a gene from bacterial and archaeocyte-derived vesicles using a sample derived from a subject, Derived vesicles that can act as a causative factor for bladder cancer were identified.

(A) extracting DNA from extracellular vesicles isolated from a sample of a subject;

(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And

(c) comparing the content of the normal-derived sample with that of the bacterium and the endocellular vesicles derived from the archaeocytes through sequencing analysis of the PCR product, thereby providing an information providing method for diagnosing bladder cancer.

The term "diagnosis of bladder cancer" used in the present invention means to determine whether a bladder cancer is likely to develop in a patient, whether the risk of bladder cancer is relatively high, or whether bladder cancer has already developed. The method of the present invention can be used to slow the onset or prevent the onset of the disease through special and appropriate management as a patient with a high risk of developing bladder cancer for any particular patient. In addition, the methods of the present invention can be used clinically to determine treatment by early diagnosis of bladder cancer and by selecting the most appropriate treatment regimen.

The term " metagenome "as used herein refers to the total of genomes including all viruses, bacteria, fungi, etc. in an isolated area such as soil, It is used as a concept of a genome to explain the identification of many microorganisms at once by using a sequencer to analyze microorganisms that are not cultured mainly. In particular, a metagenome is not a genome or a genome of a species, but a kind of mixed genome as a dielectric of all species of an environmental unit. This is a term derived from the viewpoint that when defining a species in the course of omics biology development, it functions not only as an existing species but also as a species through interaction with various species. Technically, it is the subject of techniques that analyze all DNA and RNA regardless of species, identify all species in an environment, identify interactions, and metabolism using rapid sequencing. In the present invention, metagenomic analysis was carried out preferably using extracellular vesicles derived from bacteria isolated from blood and urine.

In the present invention, the sample of the sample may be blood or urine, and the blood may preferably be whole blood, serum, plasma, or blood mononuclear cells, but is not limited thereto.

In the examples of the present invention, metagenomic analysis was carried out on the above-described extracellular vesicles derived from bacteria and archaea, and the results were analyzed on the basis of phylum, class, order, family, and genus Respectively, to identify bacterial-derived vesicles that could actually act as a cause of bladder cancer development.

More specifically, in one embodiment of the present invention, the analysis of the bacterial metagenomes on vesicles present in blood samples from the subject revealed that the content of extracellular vesicles derived from Rubrobacteria and Erysipelotrichi strong bacteria was higher in bladder cancer patients and normal persons (See Example 4).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at the neck level for vesicles present in blood samples from the subject, and as a result, Rubrobacterales, Erysipelotrichales, Burkholderiales, Enterobacteriales, Lactobacillales, Neisseriales, There was a significant difference in the contents of the outer vesicles between bladder cancer patients and normal subjects (see Example 4).

More specifically, in one embodiment of the present invention, the bacterial metagenomes are analyzed at an over-level against vesicles present in a blood sample from a subject, and as a result, The content of bacterial-derived extracellular vesicles in Enterobacteriaceae, Lachnospiraceae, Corynebacteriaceae, Deinococcaceae, Porphyromonadaceae, Neisseriaceae, Enterococcaceae, Lactobacillaceae, Tissierellaceae and Peptococcaceae was significantly different between bladder cancer patients and normal subjects (see Example 4).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at a low level against the vesicles present in blood samples from the subject. As a result, it was found that Cupriavidus, Eubacterium, Blautia, Catenibacterium, Collinsella, Geobacillus, Roseburia, Coprococcus, Faecalibacterium, The content of extracellular vesicles derived from bacteria belonging to the genus Pseudomonas, Corynebacterium, Mucispirillum, Deinococcus, Anaerococcus, Dorea, Enterococcus, Adlercreutzia, Parabacteroides, Lactobacillus, rc4-4, Peptoniphilus and Finegoldia was significantly different between bladder cancer patients and normal subjects See Example 4).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at the door level for vesicles present in urine samples from the subject, and the content of extracellular vesicles derived from cyanobacteria, gemmatimonadetes, planctomycetes, Acidobacteria, and Euryarchaeota germ There was a significant difference between the bladder cancer patients and the normal subjects (see Example 5).

More specifically, in one embodiment of the present invention, the analysis of the bacterial metagenomes on the vesicles present in urine samples from the subject revealed that the content of extracellular vesicles derived from chloroplasts, Methanobacteria, Planctomycetia, Acidobacterias, and Pedosphaerae strong bacteria There was a significant difference between the bladder cancer patients and the normal subjects (see Example 5).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at the neck level for vesicles present in the urine samples of the subject, and as a result, Stramenopiles, Streptophyta, Pseudomonadales, Turicibacterales, Methanobacteriales, Gemmatales, Acidobacteriales, The amount of extracellular vesicles was significantly different between bladder cancer patients and normal subjects (see Example 5).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at a high level against vesicles present in a urine sample from a subject, and as a result, it was found that the bacterial metagenomes were found to be at least as high as those of Flavobacteriaceae Lactobacillaceae Pseudomonadaceae Ruminococcaceae Comamonadaceae Turicibacteraceae Clostridiaceae Gordoniaceae Rikenellaceae, The content of Prevotellaceae, Methanobacteriaceae, Barnesiellaceae, Peptostreptococcaceae, and Koribacteraceae and bacterial-derived extracellular vesicles were significantly different between bladder cancer patients and normal subjects (see Example 5).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at the genus level against the vesicles present in the urine samples from the subject. As a result, Rhizobium, Proteus, Pseudomonas, Lactobacillus, Turicibacter, Ruminococcus, Klebsiella, Faecalibacterium, Brevundimonas, The content of extracellular vesicles derived from Clostridium, Jeotgalicoccus, Megasphaera, Gordonia, Prevotella, Actinobacillus, Thermoanaerobacterium and genus Methanobrevibacter was significantly different between bladder cancer patients and normal subjects (see Example 5).

The present invention is based on the results of the above-described Examples, so that bacterial-derived vesicles whose contents were significantly changed in patients with bladder cancer were identified by performing a metagenome analysis on the extracellular vesicles derived from bacteria isolated from blood and urine , And it was confirmed that bladder cancer can be diagnosed by analyzing the increase and decrease of the bacterial-derived vesicles at the above-mentioned levels through the metagenome analysis.

Hereinafter, preferred embodiments of the present invention will be described in order to facilitate understanding of the present invention. However, the following examples are provided only for the purpose of easier understanding of the present invention, and the present invention is not limited by the following examples.

[Example]

Example 1. Analysis of intestinal absorption, distribution, and excretion of intestinal bacteria and bacterial-derived vesicles

Experiments were carried out in the following manner to evaluate whether intestinal bacteria and bacterial - derived vesicles were systemically absorbed through the gastrointestinal tract. Fluorescence was measured at 0 min, 5 min, 3 hr, 6 hr, and 12 hr after administration of fluorescein-labeled intestinal bacteria and intestinal bacterial-derived vesicles in the stomach of mice to the gastrointestinal tract at a dose of 50 μg, respectively. As a result of observing the whole image of the mouse, it was not systemically absorbed when the bacterium was the bacterium as shown in Fig. 1A, but was systemically absorbed 5 minutes after the administration when it was bacterial-derived vesicle (EV) After 3 hours, the bladder was observed to be strongly fluorescent, indicating that the vesicles were excreted in the urinary tract. It was also found that the vesicles were present in the body for up to 12 hours of administration.

In order to evaluate the invasion pattern of intestinal bacteria and intestinal bacterial-derived vesicles after systemic absorption, 50 μg of fluorescently labeled bacteria and bacterial-derived vesicles were administered as described above, and after 12 hours Blood, Heart, Lung, Liver, Kidney, Spleen, Adipose tissue, and Muscle were extracted from the mouse. As a result of observing the fluorescence in the extracted tissues, as shown in Fig. 1B, the intestinal bacteria (Bacteria) were not absorbed by each organ, whereas the intestinal bacterial extracellular vesicles (EV) , Liver, kidney, spleen, adipose tissue, and muscle.

Example 2. Separation of vesicles from blood and urine and DNA extraction

To separate the vesicles from the blood and urine and extract the DNA, blood or urine was first placed in a 10 ml tube and centrifuged (3,500 xg, 10 min, 4 ° C) to resuspend the supernatant, I moved it to a tube. Bacteria and foreign substances were removed from the recovered supernatant using a 0.22 mu m filter, transferred to centripreigugal filters 50 kD, centrifuged at 1500 xg for 15 minutes at 4 DEG C to discard substances smaller than 50 kD, ≪ / RTI > After removing bacteria and debris using a 0.22 ㎛ filter, the supernatant was discarded using a Type 90 rotator at 150,000 x g for 3 hours at 4 ° C, and the supernatant was discarded. The pellet was dissolved in physiological saline (PBS) A vesicle was obtained.

100 μl of the vesicles isolated from the blood and urine were boiled at 100 ° C to allow the internal DNA to come out of the lipid, followed by cooling on ice for 5 minutes. Then, the supernatant was collected by centrifugation at 10,000 x g at 4 ° C for 30 minutes in order to remove the remaining suspension, and the amount of DNA was quantified using Nanodrop. Then, PCR was performed with the 16s rDNA primer shown in Table 1 below to confirm whether the DNA extracted from the bacterium was present in the extracted DNA to confirm that the gene derived from the bacterium was present in the extracted gene.

primer order SEQ ID NO: 16S rDNA 16S_V3_F 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3 ' One 16S_V4_R 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3 ' 2

Example 3. Metagenomic analysis using DNA extracted from blood and urine

After the gene was extracted by the method of Example 2, PCR was performed using the 16S rDNA primer shown in Table 1 to amplify the gene and perform sequencing (Illumina MiSeq sequencer). The result is output to the Standard Flowgram Format (SFF) file and the SFF file is converted into the sequence file (.fasta) and the nucleotide quality score file using the GS FLX software (v2.9) (20 bps) and less than 99% of the average base call accuracy (Phred score <20). After removing the low quality parts, only those with lead lengths of 300 bps or more were used (Sickle version 1.33). In order to analyze the results, Operational Taxonomy Unit (OTU) performed clustering according to sequence similarity using UCLUST and USEARCH. Specifically, clustering is performed based on sequence similarity of 94% for the genus, 90% for the family, 85% for the order, 80% for the class, and 75% for the phylum Bacteria with a sequence similarity of 97% or more were analyzed using the 16S DNA sequence database (108,453 sequence) of BLASTN and GreenGenes (QIIME).

Example 4. Diagnostic model of bladder cancer based on analysis of bacterium-derived vesicle meta genome isolated from blood

META genome sequencing was performed after isolating vesicles from 176 bladder blood samples of 91 bladder cancer patients and age and gender matched by the method of Example 3 above. For the development of the diagnostic model, first the p value between the two groups was less than 0.05 and the difference between the two groups was more than 2 times, and the logistic regression analysis was used to determine the diagnostic performance index AUC under curve, sensitivity, and specificity.

Analysis of bacterial-derived vesicles in the blood at the class level resulted in significant diagnostic performance for bladder cancer when the diagnostic model was developed with Rubrobacteria and Erysipelotrichi bacteria biomarkers (see Table 2 and Figure 2) .

Control group Bladder cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity AUC sensitivity specificity c__Rubrobacteria 0.0009 0.0026 0.0000 0.0000 0.0001 0.05 0.65 0.90 0.27 0.52 0.84 0.09 c__Erysipelotrichi 0.0100 0.0162 0.0013 0.0013 0.0000 0.13 0.78 0.92 0.39 0.74 0.80 0.28

Analysis of the bacterial parasites in the blood at the order level showed that diagnostic performance against bladder cancer was significant when the diagnostic model was developed with Rubobacterales, Erysipelotrichales, Burkholderiales, Enterobacteriales, Lactobacillales, Neisseriales, (See Table 3 and FIG. 3).

Control group Bladder cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity AUC sensitivity specificity o__Rubrobacterales 0.0009 0.0026 0.0000 0.0000 0.0001 0.05 0.65 0.90 0.27 0.52 0.84 0.09 o__Erysipelotrichales 0.0100 0.0162 0.0013 0.0013 0.0000 0.13 0.78 0.92 0.39 0.74 0.80 0.28 o__Burkholderiales 0.0304 0.0333 0.0066 0.0066 0.0000 0.22 0.81 0.79 0.56 0.84 0.86 0.66 o__Eterobacteriales 0.0924 0.0763 0.0377 0.0377 0.0000 0.41 0.76 0.83 0.44 0.76 0.76 0.59 o__Lactobacillales 0.0892 0.0469 0.1895 0.1895 0.0000 2.12 0.71 0.97 0.42 0.65 1.00 0.19 o__Neisseriales 0.0150 0.0439 0.0446 0.0446 0.0000 2.97 0.75 0.96 0.07 0.79 0.98 0.13 o__RF32 0.0002 0.0009 0.0026 0.0026 0.0007 11.82 0.72 0.97 0.22 0.69 0.98 0.31

Bacterial-derived vesicles in the blood were analyzed at the family level and found to be in the order of Oxalobacteraceae, Rikenellaceae, Erysipelotrichaceae, Bacillaceae, Rhizobiaceae, Fusobacteriaceae, Pseudomonadaceae, Comamonadaceae, Planococcaceae, Enterobacteriaceae, Lachnospiraceae, Corynebacteriaceae, Deinococcaceae, Porphyromonadaceae, Neisseriaceae, Enterococcaceae, Lactobacillaceae, When diagnostic models were developed with Tissierellaceae, and Peptococcaceae and bacterial biomarkers, the diagnostic performance of bladder cancer was significant (see Table 4 and Figure 4).

Control group Bladder cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity AUC sensitivity specificity f__Oxalobacteraceae 0.0134 0.0231 0.0009 0.0009 0.0000 0.06 0.80 0.74 0.68 0.85 0.86 0.50 f__Rikenellaceae 0.0025 0.0058 0.0002 0.0002 0.0000 0.09 0.70 0.99 0.02 0.63 1.00 0.00 f__Erysipelotrichaceae 0.0100 0.0162 0.0013 0.0013 0.0000 0.13 0.78 0.92 0.39 0.74 0.80 0.28 f__Bacillaceae 0.0077 0.0134 0.0015 0.0015 0.0000 0.19 0.76 0.94 0.24 0.69 0.92 0.06 f__Rhizobiaceae 0.0057 0.0099 0.0011 0.0011 0.0000 0.19 0.73 0.98 0.03 0.74 1.00 0.03 f__Fusobacteriaceae 0.0025 0.0045 0.0005 0.0005 0.0000 0.21 0.70 0.98 0.03 0.59 1.00 0.00 f__Pseudomonadaceae 0.0804 0.0860 0.0214 0.0214 0.0000 0.27 0.81 0.81 0.49 0.77 0.84 0.50 f__Comamonadaceae 0.0129 0.0253 0.0048 0.0048 0.0001 0.38 0.65 0.96 0.14 0.55 0.90 0.00 f__Planococcaceae 0.0047 0.0117 0.0019 0.0019 0.0032 0.41 0.64 0.99 0.00 0.59 1.00 0.00 f__Eterobacteriaceae 0.0924 0.0763 0.0377 0.0377 0.0000 0.41 0.76 0.83 0.44 0.76 0.76 0.59 f__Lachnospiraceae 0.0584 0.0641 0.0288 0.0288 0.0000 0.49 0.68 0.94 0.17 0.62 0.88 0.06 f__Corynebacteriaceae 0.0310 0.0396 0.0655 0.0655 0.0000 2.11 0.68 0.94 0.22 0.74 0.94 0.19 f__Deinococcaceae 0.0014 0.0042 0.0036 0.0036 0.0033 2.51 0.61 0.98 0.03 0.60 1.00 0.03 f__Porphyromonadaceae 0.0060 0.0064 0.0160 0.0160 0.0001 2.68 0.62 0.98 0.20 0.67 1.00 0.25 f__Neisseriaceae 0.0150 0.0439 0.0446 0.0446 0.0000 2.97 0.75 0.96 0.07 0.79 0.98 0.13 f__Enterococcaceae 0.0065 0.0097 0.0207 0.0207 0.0000 3.17 0.67 0.98 0.34 0.44 0.96 0.16 f__Lactobacillaceae 0.0349 0.0331 0.1276 0.1276 0.0000 3.66 0.77 0.99 0.37 0.77 0.98 0.38 f __ [Tissierellaceae] 0.0068 0.0137 0.0254 0.0254 0.0000 3.76 0.76 0.98 0.34 0.78 0.92 0.41 f__Peptococcaceae 0.0011 0.0043 0.0052 0.0052 0.0000 4.95 0.80 0.96 0.27 0.72 0.98 0.31

Bacterial-derived vesicles in the blood were analyzed at the genus level and found to be in the order of the genus level. Diagnostic performance of bladder cancer was significant when the diagnostic models were developed with Parabacteroides, Lactobacillus, rc4-4, Peptoniphilus, and Finegoldia bacterial biomarkers (see Table 5 and Figure 5).

Control group Bladder cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity AUC sensitivity specificity g__Cupriavidus 0.008 0.020 0.000 0.000 0.000 0.02 0.76 0.98 0.12 0.87 0.96 0.25 g __ [Eubacterium] 0.002 0.004 0.000 0.000 0.000 0.03 0.77 0.91 0.25 0.70 0.88 0.06 g__Blautia 0.013 0.021 0.001 0.001 0.000 0.04 0.82 0.71 0.71 0.77 0.73 0.69 g__Catenibacterium 0.006 0.014 0.000 0.000 0.000 0.04 0.77 0.91 0.25 0.67 0.82 0.19 g__Collinsella 0.006 0.012 0.000 0.000 0.000 0.07 0.80 0.82 0.54 0.68 0.71 0.47 g__Geobacillus 0.003 0.008 0.000 0.000 0.000 0.11 0.65 0.94 0.22 0.50 0.86 0.06 g__Roseburia 0.002 0.003 0.000 0.000 0.000 0.17 0.68 0.92 0.24 0.60 0.80 0.06 g__Coprococcus 0.007 0.008 0.001 0.001 0.000 0.20 0.80 0.79 0.53 0.67 0.76 0.38 g__Faecalibacterium 0.021 0.024 0.005 0.005 0.000 0.23 0.74 0.97 0.24 0.77 0.86 0.19 g__Pseudomonas 0.076 0.083 0.019 0.019 0.000 0.26 0.81 0.83 0.54 0.78 0.84 0.50 g__Corynebacterium 0.031 0.040 0.066 0.066 0.000 2.11 0.68 0.94 0.22 0.74 0.94 0.19 g__mucispirillum 0.002 0.006 0.004 0.004 0.021 2.43 0.65 0.98 0.03 0.52 1.00 0.03 g__Deinococcus 0.001 0.004 0.004 0.004 0.003 2.52 0.61 0.98 0.03 0.60 1.00 0.03 g__Anaerococcus 0.003 0.009 0.009 0.009 0.001 2.59 0.68 0.98 0.12 0.64 0.94 0.16 g__Dorea 0.003 0.004 0.008 0.008 0.002 2.66 0.67 0.94 0.25 0.57 0.88 0.22 g__Enterococcus 0.006 0.009 0.018 0.018 0.000 3.01 0.67 0.98 0.36 0.44 0.96 0.16 g__Adlercreutzia 0.002 0.004 0.006 0.006 0.000 3.07 0.67 0.94 0.10 0.68 1.00 0.13 g__Parabacteroides 0.004 0.006 0.016 0.016 0.000 3.55 0.68 0.95 0.27 0.69 0.96 0.31 g__Lactobacillus 0.034 0.033 0.127 0.127 0.000 3.70 0.77 0.99 0.37 0.77 0.98 0.38 g__rc4-4 0.001 0.004 0.005 0.005 0.000 5.30 0.80 0.96 0.27 0.73 0.98 0.31 g__Peptoniphilus 0.001 0.004 0.006 0.006 0.000 5.33 0.73 0.97 0.25 0.70 0.90 0.28 g__Finegoldia 0.002 0.008 0.010 0.010 0.000 5.70 0.75 0.98 0.32 0.76 0.98 0.28

Example 5. Diagnostic model of bladder cancer based on analysis of bacterium-derived vesicle metagenomes isolated from urine

In the method of Example 3, metagenomic sequencing was performed after separating vesicles from urine of 107 normals who matched 42 patients with bladder cancer and age and gender. For the development of the diagnostic model, first the p value between the two groups was less than 0.05 and the difference between the two groups was more than 2 times, and the logistic regression analysis was used to determine the diagnostic performance index AUC under curve, sensitivity, and specificity.

Analysis of urine bacterial parasites at the phylum level revealed that the diagnostic performance of cyanobacteria, gemmatimonadetes, planctomycetes, Acidobacteria, and Euryarchaeota germ biomarkers was significant when bladder cancer was diagnosed 6 and Fig. 6).

Control group Bladder cancer t-test Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity p__Cyanobacteria 0.0240 0.0331 0.0047 0.0053 0.0000 0.19 0.77 0.92 0.29 p__Gemmatimonadetes 0.0003 0.0010 0.0013 0.0017 0.0008 4.35 0.68 0.95 0.26 p__Planctomycetes 0.0003 0.0012 0.0014 0.0022 0.0027 4.80 0.70 0.96 0.19 p__Acidobacteria 0.0008 0.0022 0.0047 0.0077 0.0024 6.17 0.70 0.97 0.31 p__Euryarchaeota 0.0004 0.0011 0.0028 0.0030 0.0000 6.91 0.74 0.95 0.43

Analysis of urine bacterial vesicles at the class level revealed that the diagnostic performance of bladder cancer was significant when the diagnostic model was developed with Chloroplast, Methanobacteria, Planctomycetia, Acidobacteriia, and Pedosphaerae bacterium biomarkers 7 and Fig. 7).

Control group Bladder cancer t-test Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity c__Chloroplast 0.0236 0.0329 0.0041 0.0050 0.0000 0.17 0.78 0.92 0.33 c__Methanobacteria 0.0004 0.0011 0.0028 0.0030 0.0000 6.98 0.73 0.95 0.43 c__Planctomycetia 0.0001 0.0005 0.0012 0.0021 0.0033 9.39 0.73 0.96 0.24 c__Acidobacteriia 0.0001 0.0004 0.0016 0.0029 0.0014 22.66 0.69 0.99 0.29 c __ [Pedosphaerae] 0.0000 0.0000 0.0008 0.0020 0.0001 0.63 1.00 0.24

Analysis of urine bacterial parasites at the level of order showed that diagnostic tests for bladder cancer were performed when the diagnostic model was developed with Stramenopiles, Streptophyta, Pseudomonadales, Turicibacterales, Methanobacteriales, Gemmatales, Acidobacteriales, and Ellin329 bacterium biomarkers. (See Table 8 and FIG. 8).

Control group Bladder cancer t-test Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity o__Stramenopiles 0.0047 0.0074 0.0000 0.0000 0.0001 0.00 0.87 0.87 0.57 o__Streptophyta 0.0189 0.0321 0.0041 0.0050 0.0000 0.22 0.70 0.98 0.05 o__Pseudomonadales 0.1456 0.1521 0.0641 0.0372 0.0000 0.44 0.70 0.97 0.10 o__Turicibacterales 0.0014 0.0022 0.0037 0.0040 0.0010 2.66 0.70 0.92 0.19 o__Methanobacteriales 0.0004 0.0011 0.0028 0.0030 0.0000 6.98 0.73 0.95 0.43 o__Gemmatales 0.0000 0.0003 0.0010 0.0021 0.0066 19.91 0.71 0.97 0.24 o__Acidobacteriales 0.0001 0.0004 0.0016 0.0029 0.0014 22.66 0.69 0.99 0.29 o__Ellin329 0.0000 0.0000 0.0006 0.0013 0.0096 126.84 0.65 0.99 0.26

In the analysis of urine bacterial vesicles at the family level, the diagnostic performance of bladder cancer was evaluated when a diagnostic model was developed with Stramenopiles, Streptophyta, Pseudomonadales, Turicibacterales, Methanobacteriales, Gemmatales, Acidobacteriales, and Ellin329 and bacterial biomarkers. (See Table 9 and FIG. 9).

Control group Bladder cancer t-test Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity f__Flavobacteriaceae 0.0028 0.0038 0.0004 0.0009 0.0000 0.15 0.72 1.00 0.02 f__Lactobacillaceae 0.0507 0.0497 0.0216 0.0115 0.0000 0.43 0.68 0.95 0.07 f__Pseudomonadaceae 0.0765 0.0775 0.0348 0.0237 0.0000 0.45 0.74 0.92 0.12 f__Ruminococcaceae 0.0567 0.0706 0.1340 0.0720 0.0000 2.36 0.84 0.92 0.33 f__Comamonadaceae 0.0054 0.0095 0.0135 0.0100 0.0000 2.48 0.80 0.94 0.24 f__Turicibacteraceae 0.0014 0.0022 0.0037 0.0040 0.0010 2.66 0.70 0.92 0.19 f__Clostridiaceae 0.0077 0.0105 0.0208 0.0180 0.0001 2.69 0.77 0.87 0.31 f__Gordoniaceae 0.0001 0.0005 0.0008 0.0013 0.0020 5.73 0.69 0.96 0.24 f__Rikenellaceae 0.0011 0.0029 0.0064 0.0064 0.0000 5.87 0.84 0.95 0.48 f__Prevotellaceae 0.0102 0.0121 0.0609 0.0785 0.0002 5.95 0.93 0.94 0.74 f__Methanobacteriaceae 0.0004 0.0011 0.0028 0.0030 0.0000 6.98 0.73 0.95 0.43 f __ [Barnesiellaceae] 0.0002 0.0008 0.0018 0.0038 0.0096 9.42 0.71 0.97 0.36 f__Peptostreptococcaceae 0.0010 0.0020 0.0097 0.0085 0.0000 9.64 0.87 0.95 0.62 f.Koribacteraceae 0.0001 0.0004 0.0015 0.0026 0.0009 21.66 0.69 0.99 0.29

Analysis of urine bacterial parasites at the genus level revealed that the vesicles from Rhizobium, Proteus, Pseudomonas, Lactobacillus, Turicibacter, Ruminococcus, Klebsiella, Faecalibacterium, Brevundimonas, Clostridium, Jeotgalicoccus, Megasphaera, Gordonia, Prevotella, Actinobacillus, Thermoanaerobacterium and Methanobrevibacter When diagnostic models were developed with genus bacterial biomarkers, the diagnostic performance of bladder cancer was significant (see Table 10 and Figure 10).

Control group Bladder cancer t-test Taxon Mean SD Mean SD p-value Ratio AUC sensitivity specificity g__Rhizobium 0.0044 0.0068 0.0001 0.0003 0.0000 0.01 0.87 0.89 0.57 g__Proteus 0.0197 0.0287 0.0015 0.0027 0.0000 0.07 0.81 0.91 0.45 g__Pseudomonas 0.0736 0.0770 0.0267 0.0203 0.0000 0.36 0.79 0.87 0.31 g__Lactobacillus 0.0503 0.0497 0.0208 0.0114 0.0000 0.41 0.69 0.94 0.12 g__Turicibacter 0.0014 0.0022 0.0037 0.0040 0.0010 2.66 0.70 0.92 0.19 g__Ruminococcus 0.0071 0.0134 0.0188 0.0173 0.0002 2.67 0.77 0.95 0.24 g__Klebsiella 0.0015 0.0038 0.0040 0.0038 0.0005 2.67 0.76 0.93 0.17 g__Faecalibacterium 0.0181 0.0330 0.0488 0.0354 0.0000 2.70 0.80 0.92 0.24 g__Brevundimonas 0.0004 0.0010 0.0010 0.0014 0.0073 2.85 0.68 0.95 0.12 g__Clostridium 0.0016 0.0049 0.0073 0.0130 0.0088 4.62 0.72 0.97 0.14 g__Jeotgalicoccus 0.0011 0.0023 0.0055 0.0078 0.0008 5.06 0.71 0.94 0.31 g__Megasphaera 0.0003 0.0011 0.0013 0.0028 0.0251 5.15 0.63 0.99 0.14 g__Gordonia 0.0001 0.0005 0.0008 0.0013 0.0020 5.73 0.69 0.96 0.24 g__Prevotella 0.0102 0.0121 0.0609 0.0785 0.0002 5.95 0.93 0.94 0.74 g__Actinobacillus 0.0002 0.0006 0.0010 0.0018 0.0037 6.64 0.66 0.97 0.21 g__Thermoanaerobacterium 0.0002 0.0013 0.0013 0.0050 0.1583 6.91 0.61 0.98 0.05 g__Methanobrevibacter 0.0002 0.0007 0.0018 0.0025 0.0003 9.15 0.74 0.97 0.26

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. There will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

<110> MD Healthcare Inc. <120> Method for diagnosis of bladder cancer using analysis of          마이크로 비아 메agenome <130> MP17-337 <150> KR 1020160179240 <151> 2016-12-26 <160> 2 <170> KoPatentin 3.0 <210> 1 <211> 50 <212> DNA <213> Artificial Sequence <220> <223> 16S_V3_F <400> 1 tcgtcggcag cgtcagatgt gtataagaga cagcctacgg gnggcwgcag 50 <210> 2 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> 16S_V4_R <400> 2 gtctcgtggg ctcggagatg tgtataagag acaggactac hvgggtatct aatcc 55

Claims (2)

(a) extracting DNA from an extracellular vesicle isolated from a sample of a subject;
(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And
(c) comparing the increase or decrease in the content of the sample derived from the normal person with the amount of the bacterial and the endocellular vesicles derived from the archaeocyte through sequence analysis of the PCR product,
The sample of the sample is blood or urine,
(Cyanobacteria, Gemmatimonadetes, Planctomycetes, Acidobacteria, and Euryarchaeota) isolated from the urine sample of the subject in step (c) One or more phylum bacterial-derived extracellular vesicles selected from the group,
The test specimens were prepared from Rubobacteria, Erysipelotrichi, Chloroplast, Methanobacteria, and Planckomycetes isolated from the blood samples of the subject. Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Planctomycetia, Acidobacteriia, and Pedosphaerae,
The method of any one of claims 1 to 3, wherein the sample is selected from the group consisting of Rubrobacterales, Erysipelotrichales, Burkholderiales, Enterobacteriales, Lactobacillales, Neisseriales, Stramenopiles, Streptophyta, Pseudomonadales, Turicibacterales, Methanobacterials (Streptomyces) isolated from the urine samples of the above- One or more order bacterial-derived extracellular vesicles selected from the group consisting of Methanobacteriales, Gemmatales, and Acidobacteriales,
The method of any one of claims 1 to 3, wherein the sample is selected from the group consisting of Oxalobacteraceae, Rikenellaceae, Erysipelotrichaceae, Bacillaceae, Rhizobiaceae, But are not limited to, bacteria such as Fusobacteriaceae, Pseudomonadaceae, Comamonadaceae, Planococcaceae, Enterobacteriaceae, Lachnospiraceae, It is known that the bacteria belonging to the genus Corynebacteriaceae, Deinococcaceae, Porphyromonadaceae, Neisseriaceae, Enterococcaceae, Lactobacillaceae, Tissierellaceae, Peptococcaceae, Flavobacteriaceae, Lactobacillaceae, Pseudomonadaceae isolated from the urine samples of the above-mentioned samples, e), Ruminococcaceae, Comamonadaceae, Turicibacteraceae, Clostridiaceae, Gordoniaceae, Lychenelaciae, (Rikenellaceae), Prevotellaceae, Methanobacteriaceae, Barnesiellaceae, Peptostreptococcaceae, and Koribacteraceae), and the like. One or more family bacterial-derived extracellular vesicles selected from the group consisting of
The present invention relates to a method for screening a sample of a blood sample of a subject such as Cupriavidus, Eubacterium, Blautia, Catenibacterium, Collinsella, Geobacillus, But are not limited to, Roseburia, Coprococcus, Faecalibacterium, Pseudomonas, Corynebacterium, Mucispirillum, Deinococcus, His wife has been found to be a member of the genus Anaerococcus, Dorea, Enterococcus, Adlercreutzia, Parabacteroides, Lactobacillus, Peptoniphilus, Rhizobium, Proteus, Pseudomonas, Lactobacillus, Turicibacter, Ruminococcus, Klebsiophilus isolated from the urine samples of the above- Klebs icel, Faecalibacterium, Brevundimonas, Clostridium, Jeotgalicoccus, Megasphaera, Gordonia, Prevotella, ) Compared with the increase or decrease in the content of one or more genus bacterial extracellular vesicles selected from the group consisting of Actinobacillus, Thermoanaerobacterium, and Methanobrevibacter Wherein the method comprises the steps of:
The method according to claim 1,
Wherein the blood is whole blood, serum, plasma, or blood mononuclear cells.

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