KR102334433B1 - Method for diagnosis of brain tumor using analysis of bacteria metagenome - Google Patents

Abstract

The present invention relates to a method for diagnosing brain tumors through bacterial metagenome analysis, and more specifically, by performing bacterial metagenome analysis using samples of normal persons and subjects to analyze the increase or decrease in the content of specific bacteria-derived extracellular vesicles, thereby diagnosing brain tumors it's about how to Extracellular vesicles secreted from bacteria present in the environment are absorbed into the body and can have a direct effect on inflammation. Accordingly, by diagnosing the risk of brain tumor in advance through metagenome analysis of extracellular vesicles derived from bacteria using a human sample according to the present invention, it is possible to diagnose and predict the risk group of autism early, and also, through appropriate management, the onset time It can delay or prevent the onset of the disease. In addition, it is possible to diagnose early even after the onset of the disease, thereby lowering the incidence of brain tumors and increasing the therapeutic effect. There are advantages to preventing it.

Description

Brain tumor diagnosis method through bacterial metagenome analysis {Method for diagnosis of brain tumor using analysis of bacteria metagenome}

The present invention relates to a method for diagnosing brain tumors through bacterial metagenome analysis, and more specifically, by performing bacterial metagenome analysis using samples derived from normal individuals and subjects to analyze the increase or decrease in the content of specific bacteria-derived extracellular vesicles. How to diagnose, etc.

Brain tumor is a tumor that occurs in the brain and central nervous system and can be used interchangeably with "brain cancer" It is the second most common tumor among tumors. The incidence of brain tumors is about 10 per 100,000 people, and gliomas, the most common primary brain tumors, account for about 35% of the total and are malignant. Mutagenic substances known so far as one of the causes of brain tumors include radiation, chemicals, viruses, etc. It is known that the incidence of brain tumors increases even when the immune function is lowered like AIDS. Recently, due to environmental pollution, etc., interest in the relationship between chemical substances and cancer occurrence is increasing, but a clear causal relationship between the occurrence of brain tumors has not been established.

The mechanisms that cause clinical symptoms of brain tumors include symptoms caused by increased intracranial pressure, symptoms caused by brain tumors compressing the surrounding brain, symptoms caused by brain tumors electrically stimulating surrounding cranial nerves (epileptic seizures), and symptoms caused by hormonal abnormalities. Included. In the case of not receiving active treatment, both malignant and benign brain tumors can lead to fatal results.

As a method for determining the prognosis of existing brain tumor patients, it is common to synthesize the course of clinical treatment, radiation therapy, and chemotherapy, and to determine the survival prognosis, including the possibility of recurrence, in consideration of the malignancy, location, and age of the brain tumor. However, this method cannot be said to be an accurate prognostic prediction method because there are various differences for each type of patient and results for clinical treatment are also different. Therefore, the need for developing a biomarker as a new diagnostic and therapeutic target capable of diagnosing brain tumors and predicting the prognosis is emerging.

On the other hand, the microbiota or microbiome refers to the microbial community including bacteria, archaea, and eukaryotes that exist in a given habitat, and the gut microbiota affects human physiology. It plays an important role and is known to have a significant impact on human health and disease through interaction with human cells. The symbiotic bacteria in our body secrete nanometer-sized vesicles to exchange information such as genes and proteins with other cells. The mucous membrane forms a physical barrier that does not allow particles larger than 200 nanometers (nm) to pass through. However, bacteria-derived vesicles are relatively small because they are usually less than 100 nanometers in size. It freely passes through the mucous membrane and is absorbed into the body.

Metagenomics, also called environmental genomics, is the analysis of metagenomic data obtained from samples taken from the environment. Recently, it has become possible to catalog the bacterial composition of the human microbiota by a method based on the 16s ribosomal RNA (16s rRNA) sequencing. , NGS) platform to analyze. However, in the development of brain tumors, there has been no report on a method of identifying the causative factors of brain tumors and predicting brain tumors through metagenome analysis present in bacterial vesicles from human materials such as blood.

Domestic Patent Publication No. 2011-073049

The present inventors extracted genes from bacteria-derived extracellular vesicles present in blood, which are samples derived from normal individuals and subjects, in order to diagnose in advance the causative factors and the risk of brain tumors, and performed metagenome analysis on them. As a result, the cause of brain tumors Bacterial-derived extracellular vesicles that can act as factors were identified, and the present invention was completed based on this.

Accordingly, an object of the present invention is to provide an information providing method for diagnosing a brain tumor through a metagenome analysis of bacterial-derived extracellular vesicles, a method for diagnosing a brain tumor, and a method for predicting the risk of developing a brain tumor.

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

In order to achieve the object of the present invention as described above, the present invention provides a method for providing information for brain tumor diagnosis, comprising the following steps:

(a) extracting DNA from extracellular vesicles isolated from normal and subject samples;

(b) performing a polymerase chain reaction (PCR) on the extracted DNA using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2; and

(c) comparing the increase or decrease in the content of a normal human-derived sample and a bacterial-derived extracellular vesicle through sequencing of the PCR product.

In addition, the present invention provides a method for diagnosing a brain tumor, comprising the steps of:

(a) extracting DNA from extracellular vesicles isolated from normal and subject samples;

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

(c) comparing the increase or decrease in the content of a normal human-derived sample and a bacterial-derived extracellular vesicle through sequencing of the PCR product.

In addition, the present invention provides a method for predicting the risk of developing a brain tumor, comprising the steps of:

(a) extracting DNA from extracellular vesicles isolated from normal and subject samples;

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

(c) comparing the increase or decrease in the content of a normal human-derived sample and a bacterial-derived extracellular vesicle through sequencing of the PCR product.

In one embodiment of the present invention, in the step (c), Actinobacteria, Proteobacteria, Firmicutes, Bacteroidetes, Cyanobacteria, Saccharibacteria , and Tenericutes (Tenericutes) can be compared to the increase or decrease in the content of extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of.

In another embodiment of the present invention, in the step (c), the Clostridium class (Clostridia), the Basil class (Bacill), the Erysipelotrichia class (Erysipelotrichiam), the gamma proteobacteria (Gammaproteobacteria), alpha proteobacteria ( 1 selected from the group consisting of Alphaproteobacteria, Negativicutes, Saccharibacteria (p), Chloroplast, Actinobacteria and Mollicutes. The increase or decrease in the content of extracellular vesicles derived from more than one species of class bacteria can be compared.

In another embodiment of the present invention, in the step (c), Clostridiales, Bifidobacteriales, Erysipelotrichales, Lactobacillales, Actinomycetes ( Micrococcales), Sphingomonadales, Selenomonadales, Chloroplast (c), Saccharibacteria (p), Turicibacterales and An increase or decrease in the content of extracellular vesicles derived from one or more orders selected from the group consisting of Xanthomonadales can be compared.

In another embodiment of the present invention, in step (c), Ruminococcaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, Streptococcus Family (Streptococcaceae), Bifidobacteriaceae (Sphingomonadaceae), Prophyromonadaceae (Porphyromonadaceae), Bacteroidaceae (Bacteroidaceae), Bacteroides S24-7 (Bacteroidales S24-7) Chloroplast (c), Prevotellaceae, Saccharibacteria (p), Intrasporangiaceae, Lactobacillaceae, Mojibacteriaceae ( From the group consisting of Mogibacteriaceae, Turicibacteraceae, Nocardiaceae, Xanthomonadaceae, Weeksellaceae, and Veillonellaceae The increase or decrease in the content of extracellular vesicles derived from bacteria from one or more selected families can be compared.

In another embodiment of the present invention, in the step (c), Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Ruminiclostridium (Rumiclostridium) 6, Peptoclostridium, Eubacterium coprostanoligenes, Escherichia-Shigella, Blautia, Bifidobacterium ), Streptococcus (Streptococcus), Bacteroides (Bacteroides), Erysipelatoclostridium (Erysipelatoclostridium), Bacteroidales S24-7, Chloroplast (C), Prevotella (Prevotella) 9, Candidatus Saccharimonas, Stenotrophomonas, Lactobacillus, Turicibacter, Tepidimonas, Sphingomonas) , Rhodococcus, and Cloacibacterium can be compared to increase or decrease the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of.

In another embodiment of the present invention, in step (c), Actinobacteria, Proteobacteria, Firmicutes, Bacteroidetes, Cyanobacteria, Saccharibacteria ), and one or more phylum bacteria-derived extracellular vesicles selected from the group consisting of Tenericutes,

Clostridia, Bacill, Erysipelotrichiam, Gammaproteobacteria, Alphaproteobacteria, Negativicutes, Sacchari Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Saccharibacteria (p), Chloroplast, Actinobacteria and Mollicutes;

Clostridiales, Bifidobacteriales, Erysipelotrichales, Lactobacillales, Micrococcales, Sphingomonadales, Selenomonas (Selenomonadales), Chloroplast (c), Saccharibacteria (p)), Turicibacterales (Turicibacterales) and Xanthomonadales (Xanthomonadales) 1 species selected from the group consisting of Extracellular vesicles derived from bacteria of the above order,

Ruminococcaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, Streptococcidae, Sphingae, Bifidobacteriaceae Sphingomonadaceae, Porphyromonadaceae, Bacteroidaceae, Bacteroidales S24-7, Chloroplast (c), Prevotellaceae ), Saccharibacteria (p), Intrasporangiaceae, Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nocar Extracellular vesicles derived from one or more family bacteria selected from the group consisting of Nocardiaceae, Xanthomonadaceae, Weekselaceae, and Veillonellaceae, or

Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Ruminiclostridium 6, Peptoclostridium, Eubacterium Coprostanorigenes ([Eubacterium] coprostanoligenes), Escherichia-Shigella, Blautia, Bifidobacterium, Streptococcus, Bacteroides, Erysipelatoclostridium, Bacteroidales S24-7, Chloroplast (c), Prevotella 9, Candidatus Saccharimonas, Stenotrophomonas, Lactobacillus, Turicibacter, Tepidimonas, Sphingomonas, Rhodococcus, and Cloacibacterium The increase or decrease in the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of can be compared.

In another embodiment of the present invention, in step (c), compared with a sample derived from a normal person,

Extracellular vesicles derived from bacteria of the phylum Tenericutes,

Extracellular vesicles derived from bacteria of the class Mollicutes,

Extracellular vesicles derived from bacteria of the order Turicibacterales,

At least one species selected from the group consisting of Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nocardiaceae, and Weekselaceae extracellular vesicles derived from family bacteria, or

Cells derived from one or more genus bacteria selected from the group consisting of Lactobacillus, Turicibacter, Tepidimonas, Rhodococcus, and Cloacibacterium If the content of extravesicles is increased, it can be diagnosed as a brain tumor.

In another embodiment of the present invention, in step (c), compared with a sample derived from a normal person,

Extracellular vesicles derived from bacteria of the Actinobacteria phylum,

Actinobacteria class bacteria-derived extracellular vesicles,

Extracellular vesicles derived from bacteria of the order Xanthomonadales,

Extracellular vesicles derived from one or more family bacteria selected from the group consisting of Intrasporangiaceae, Xanthomonadaceae, and Veillonellaceae, or

When the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of Stenotrophomonas and Sphingomonas is reduced, brain tumor can be diagnosed.

In another embodiment of the present invention, in step (c), compared with a sample derived from a normal person,

Firmicutes-derived extracellular vesicles,

Extracellular vesicles derived from one or more class bacteria selected from the group consisting of Clostridia, Bacill, and Erysipelotrichiam;

Extracellular vesicles derived from at least one order selected from the group consisting of Clostridiales, Bifidobacteriales, and Erysipelotrichales;

Ruminococcaceae, Lactobacillaceae, Peptostreptococcaceae, and Erysipelotrichaceae derived from at least one family of bacteria-derived extracellular parcel, or

Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Lactobacillus, Ruminiclostridium 6, Peptoclostridium 6 When the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of (Peptoclostridium) is increased, brain tumor can be diagnosed.

In another embodiment of the present invention, in step (c), compared with a sample derived from a normal person,

Extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of Actinobacteria and Proteobacteria;

Extracellular vesicles derived from at least one class of bacteria selected from the group consisting of Gammaproteobacteria, Actinobacteria, Alphaproteobacteria, and Negativicutes;

Extracellular vesicles derived from at least one order selected from the group consisting of Lactobacillales, Micrococcales, Sphingomonadales, and Selenomonadales;

Lachnospiraceae (Lachnospiraceae), streptococcaceae (Streptococcaceae), Bifidobacteriaceae (Bifidobacteriaceae), Sphingomonadaceae (Sphingomonadaceae), and at least one species selected from the group consisting of Porphyromonadaceae (Porphyromonadaceae) family) bacterial-derived extracellular vesicles, or

Eubacterium coprostanoligenes ([Eubacterium] coprostanoligenes), Escherichia-Shigella, Blautia, Bifidobacterium, Streptococcus, and Sphingomonas ( When the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of Sphingomonas) is reduced, it can be diagnosed as a brain tumor.

In another embodiment of the present invention, in step (c), compared with a sample derived from a normal person,

Extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of Actinobacteria, Proteobacteria, Firmicutes, and Bacteroidetes;

Erysipelotrichiam bacteria-derived extracellular vesicles,

Erysipelotrichales bacteria-derived extracellular vesicles,

Extracellular vesicles derived from one or more family bacteria selected from the group consisting of Erysipelotrichaceae and Bacteroidaceae, or

When the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of Bacteroides and Erysipelatoclostridium is increased, brain tumor can be diagnosed.

In another embodiment of the present invention, in step (c), compared with a sample derived from a normal person,

Cyanobacteria, and extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of Saccharibacteria;

Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Clostridia, Saccharibacteria (p), and Chloroplast;

Extracellular vesicles derived from at least one order selected from the group consisting of Clostridiales, Chloroplast (c), and Saccharibacteria (p);

Ruminococcaceae, Bacteroidales S24-7, Chloroplast (c), Prevotellaceae, and Saccharibacteria (p) Extracellular vesicles derived from one or more family bacteria selected from the group consisting of, or

consisting of Bacteroidales S24-7, Chloroplast (c), Lachnospiraceae NK4A136, Prevotella 9, and Candidatus Saccharimonas Brain tumor can be diagnosed when the content of extracellular vesicles derived from one or more genus bacteria selected from the group is reduced.

In another embodiment of the present invention, the normal human and subject samples may be blood or tissue.

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

In another embodiment of the present invention, the tissue may be brain tissue.

Extracellular vesicles secreted from bacteria present in the environment are absorbed into the body and can have a direct effect on inflammation. Accordingly, by diagnosing the risk of brain tumor in advance through metagenome analysis of extracellular vesicles derived from bacteria using a human sample according to the present invention, it is possible to diagnose and predict the risk group of autism early, and also, through appropriate management, the onset time It can delay or prevent the onset of the disease. In addition, it is possible to diagnose early even after the onset of the disease, thereby lowering the incidence of brain tumors and increasing the therapeutic effect. There are advantages to preventing it.

1A and 1B show (a) Alpha diversity (Chao1, Shannon and Simpson indices) and (b) beta diversity.
2A-2D show differences in microbiome abundance in serum between healthy control group (HC) and brain tumor patients (BT) at phylum and genus level. (a) shows the abundance heatmap at the phylum level of serum microbial EVs, (b) shows the abundance heatmap at the genus level of serum microbial EVs, and (c) shows the major microbial EVs by t-test At the phylum level, (d) is shown at the genus level.
3a and 3b show significantly different microbial EVs determined through LEFSe analysis at the phylum level (a) and genus level (b).
4A to 4C show the abundance of microbiome in serum at (a) class, (b) order, and (c) family level.
5A and 5B show a brain tumor diagnostic model based on the serum EV microbiome at the genus level. (a) shows the ROC curves for M1 (red), M2 (blue), M3 (green), and M4 (yellow), and (b) shows the ROC curves for M5.
6A-6E show the difference in microbiome abundance between healthy control group (HC) and brain tumor patients (BT) in brain tissue at the phylum and genus level, (a) and (b) respectively. and heatmaps of key microbial EV taxa in brain tissue at the genus level, (c) and (d) are the major microbial EVs by t-test at the phyla and genus level, respectively, and (e) significantly different determined through LEfSe analysis Microbial EV biomarkers are shown at the genus level.
7a to 7c show the abundance of the microbiome at the (a) class, (b) order, (c) and (family) levels in brain tissue.
8A to 8D show the fold-change between healthy control group (HC) and brain tumor patients (BT) of serum and tissue at (a) phylum, (b) clavicle, (c) neck, and (d) family levels. will be. Red indicates significantly different microbial EV genera between clinical groups in serum, green indicates significantly different microbial EV genera between clinical groups in brain tissue, and blue indicates significantly different microbial EV genera between clinical groups in serum and tissue samples. The genus of the microbial EV is shown.
9 shows the fold-change between healthy control group (HC) and brain tumor patients (BT) in serum and tissue at the genus level. Red indicates significantly different microbial EV genera between clinical groups in serum, green indicates significantly different microbial EV genera between clinical groups in brain tissue, and blue indicates significantly different microbial EV genera between clinical groups in serum and tissue samples. The genus of the microbial EV is shown.
10 is a result showing the distribution of bacterial-derived vesicles (EVs) with significant diagnostic performance at the phylum level by performing metagenome analysis after separating bacterial-derived vesicles from the blood of brain tumor patients and normal people.
11 is a result showing the distribution of bacterial-derived vesicles (EVs) with significant diagnostic performance at the class level by performing metagenome analysis after separating bacterial-derived vesicles from the blood of brain tumor patients and normal people.
12 is a result showing the distribution of bacterial-derived vesicles (EVs) with significant diagnostic performance at the order level by performing metagenome analysis after separating bacterial-derived vesicles from the blood of brain tumor patients and normal people.
13 is a result showing the distribution of bacterial-derived vesicles (EVs) having significant diagnostic performance at the family level by performing metagenome analysis after isolating bacterial-derived vesicles from the blood of brain tumor patients and normal people.
14 is a result showing the distribution of bacterial-derived vesicles (EVs) with significant diagnostic performance at the genus level by performing metagenome analysis after separating bacterial-derived vesicles from the blood of brain tumor patients and normal people.

The present invention relates to a method for diagnosing brain tumors through bacterial metagenome analysis, and the present inventors extracted genes from bacteria-derived extracellular vesicles using samples derived from normal individuals and subjects and performed metagenome analysis on them, and the cause of brain tumors Bacterial-derived extracellular vesicles that can act as factors were identified.

Accordingly, the present invention comprises the steps of (a) extracting DNA from extracellular vesicles isolated from normal and subject samples;

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

(c) provides an information providing method for diagnosing brain tumors, including the step of comparing the increase or decrease in the content of a normal human-derived sample and a bacterial-derived extracellular vesicle through sequencing of the PCR product.

As used herein, the term "diagnosing brain tumor" means determining whether there is a possibility of developing a brain tumor for a patient, a relatively high probability of developing a brain tumor, or whether a brain tumor has already occurred. The method of the present invention can be used to delay the onset of or prevent the onset of a brain tumor through special and appropriate management as a patient with a high risk of brain tumor for any specific patient. In addition, the method of the present invention can be used clinically to determine a treatment by diagnosing a brain tumor at an early stage and selecting the most appropriate treatment method.

As used herein, the term "metagenome" is also referred to as "microgenome", and refers to the sum of genomes including all viruses, bacteria, fungi, etc. in isolated areas such as soil and animal intestines, It is mainly used as a concept of genome to explain the identification of many microorganisms at once using a sequencer to analyze microorganisms that cannot be cultured. In particular, the metagenome does not refer to the genome or genome of one species, but a kind of mixed genome as the genome of all species in one environmental unit. This is a term derived from the point of view that, when defining a species in the process of omics development of biology, not only one functionally existing species but also various species interact with each other to create a complete species. Technically, it is the subject of a technique that uses rapid sequencing, analyzes all DNA and RNA regardless of species, identifies all species in one environment, and identifies interactions and metabolism. In the present invention, metagenome analysis was preferably performed using extracellular vesicles derived from bacteria isolated from serum.

In the present invention, the samples of normal subjects and subjects may be blood, and the blood may be whole blood, serum, plasma, or blood mononuclear cells, but is not limited thereto.

In the present invention, the normal and subject samples may be tissue, and the tissue may be brain tissue, but is not limited thereto.

In an embodiment of the present invention, metagenome analysis was performed on the bacteria-derived extracellular vesicles, and analysis was performed at the phylum, class, order, family, and genus levels, respectively. Thus, bacteria-derived vesicles that can actually cause brain tumors were identified (see Table 8 of the present invention).

More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome for vesicles present in the subject-derived serum sample,

At the phylum level, Firmicutes, Actinobacteria, Proteobacteria,

At the class level, Clostridia, Bacill, Erysipelotrichia, Gammaproteobacteria, Actinobacteria, Alphaproteobacteria, Negativicutes,

At the order level, Clostridiales, Bifidobacteriales, Erysipelotrichales, Lactobacillales, Micrococcales, Sphingomonadales, Selenomonadales,

At the family level, Ruminococcaceae, Lactobacillaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, Streptococcaceae, Bifidobacteriaceae, Sphingomonadaceae, Porphyromonadaceae,

At genus level, Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Lactobacillus, Ruminiclostridium 6, Peptoclostridium, [Eubacterium] coprostanoligenes, Escherichia-Shigella, Blautia, Streptococcus, Scherichia-Shigella, Blautia, Bifidococcus

There was a significant difference in the content of bacterial-derived extracellular vesicles between brain tumor patients and normal people (see Example 3).

More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome for vesicles present in the subject-derived brain tissue sample,

At the phylum level, Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Cyanobacteria, Saccharibacteria,

At the class level, Erysipelotrichia, Clostridia, Saccharibacteria (p), Chloroplast

At the level of the order, Erysipelotrichales, Clostridiales, Chloroplast (c), Saccharibacteria (p),

At the family level, Bacteroidaceae, Erysipelotrichaceae, Ruminococcaceae, group Bacteroidales S24-7, Chloroplast (c), Prevotellaceae, Saccharibacteria (p),

At the genus level, Bacteroides, Erysipelatoclostridium, Bacteroidales S24-7 group, Chloroplast (c), Lachnospiraceae NK4A136 group, Prevotella 9, Candidatus Saccharimonas

There was a significant difference in the content of bacteria-derived extracellular vesicles between brain tumor patients and normal people (see Example 5),

More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome at the phylum level for vesicles present in a blood sample derived from a subject, the content of extracellular vesicles derived from Actinobacteria and Tenericutes phylum bacteria was There was a significant difference between brain tumor patients and normal people (see Example 6).

More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome at the class level for vesicles present in a blood sample, the content of extracellular vesicles derived from Actinobacteria and Mollicutes class bacteria was found in brain tumor patients. and there was a significant difference between normal people (see Example 6).

More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome at the order level for vesicles present in a blood sample derived from a subject, Turicibacterales, Xanthomonadales, and RF39 order bacteria-derived extracellular vesicles There was a significant difference between the content of brain tumor patients and normal people (see Example 6).

More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome at the family level for vesicles present in a blood sample derived from a subject, Intrasporangiaceae, Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nocardiaceae, Xanthomonadaceae, Weeksellaceae, and The content of extracellular vesicles derived from Veillonellaceae family bacteria was significantly different between brain tumor patients and normal people (see Example 6).

More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome at the genus level for vesicles present in a blood sample derived from a subject, Stenotrophomonas, Lactobacillus, Turicibacter, Tepidimonas, Sphingomonas, Rhodococcus, and Cloacibacterium genera (genus) There was a significant difference in the content of bacterial-derived extracellular vesicles between brain tumor patients and normal people (see Example 6).

It was confirmed that the distribution parameters of the identified bacteria-derived extracellular vesicles could be usefully used for predicting the occurrence of brain tumors through the results of the above Examples.

Hereinafter, preferred examples are presented to help the understanding of the present invention. However, the following examples are only provided for easier understanding of the present invention, and the contents of the present invention are not limited by the following examples.

[Example]

Experimental Example 1. Subject and sample collection

Serum samples were collected from a total of 182 brain tumor (BT) patients and 125 healthy control (HC) subjects from Seoul National University Hospital and Inje University Haeundae Hospital, respectively. In addition, tissue samples from 5 BT patients and 5 HC subjects collected at Seoul National University Hospital were evaluated. Each BT clinical subject presented symptoms that led to a visit to the hospital for treatment. Health care subjects were selected through a general health examination. The experimental example of the present invention was approved by the Seoul National University Hospital Clinical Trial Review Committee (IRB No. H-1009-025-331) and Inje University Haeundae Hospital (IRB No. 1297992-2015-064). All methods of the present invention were performed according to approved guidelines, and informed consent was obtained from all clinical subjects.

All collected human serum samples were transferred to a serum separator tube (SST) and then centrifuged at 3,000 rpm at 4 °C for 15 min. All brain tissue samples were frozen in liquid nitrogen and stored at -80°C for analysis.

Experimental Example 2. In vivo mouse research model

All mice used were 6-week-old female C57BL/6 mice (Orient Bio Inc, Seongnam, Korea). Mice were housed and maintained in standard laboratory conditions of 22±2° C. and 50±5% humidity with a 12 h day and night cycle throughout the course of the in vivo study.

Experimental Example 3. EV DNA Extraction and Sequencing

To extract EVs from serum and tissue samples, blood was put into a 10 ml tube, centrifuged (3,500 x g, 10 min, 4° C.) to settle the suspension, and only the supernatant was recovered, and then transferred to a new 10 ml tube. After removing bacteria and foreign substances from the recovered supernatant using a 0.22 μm filter, transfer to a centripreigugal tube (centripreigugal filters 50 kD) and centrifuge at 1500 x g, 4°C for 15 minutes, discarding substances smaller than 50 kD and 10 ㎖ concentrated until After removing bacteria and foreign substances using a 0.22 μm filter once again, discard the supernatant using a high-speed centrifugation method at 150,000 x g, 4°C for 3 hours with a Type 90ti rotor, and dissolve the lumped pellet with physiological saline (PBS). A vesicle was obtained.

100 μl of vesicles isolated from blood according to the above method was boiled at 100° C. to release DNA from the inside out of lipids, and then cooled on ice for 5 minutes. Next, in order to remove the remaining suspended matter, centrifugation was performed at 10,000 x g, 4°C for 30 minutes, and only the supernatant was collected and the amount of DNA was quantified using Nanodrop. Thereafter, PCR was performed with the 16s rDNA primer shown in Table 1 below to confirm whether bacterial-derived DNA was present in the extracted DNA, and it was confirmed that the bacterial-derived gene 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

Experimental Example 4. Metagenomic analysis of microbial EV composition

After extracting the gene by the method of Experimental Example 3, PCR was performed using the 16S rDNA primer shown in Table 1 to amplify the gene and perform sequencing (Illumina MiSeq sequencer). Taxonomic assignment was performed with the profiling program MDx-Pro ver.2 (MD Healthcare, Korea). Paired-end reads were filtered according to barcode, primer sequences were trimmed using Cutadapt (version 1.1.6), and then merged with CASPER. To obtain high quality sequencing reads, reads less than 350 bp or greater than 550 bp, and sequences with a Phred quality score of less than 20 were excluded. Using the VSEARCH de novo clustering method, OTUs (Operational Taxonomic Units) were classified at the genus level based on a 97% similarity threshold. OTUs containing only one sequence in one sample were excluded from further analysis. Then, classification level assignment was performed at species level using UCLUST and QIIME 1.9.1 for Silva 132 database under basic parameters. If there was insufficient taxonomic information in the database to assign a cluster at the genus level, the taxon was assigned to the next highest level. Parentheses around taxon names indicate proposed taxonomic assignments that have not been identified, primarily based on whole-genome phylogenies in genomic databases.

Experimental Example 5. Establishment of a predictive diagnostic model

For the development of a brain tumor (BT, Brain Tumor) predictive diagnostic model, the relative abundance of OTUs at the genus level was considered as a model variable.

First, candidate strains with a p value of less than 0.01, a fold change greater than two-fold, and an average relative abundance greater than 0.1% were selected. A strain selected as a model variable was compared to determine the model with the highest AUC (Area Under the Curve), sensitivity, specificity, and accuracy.

The first method (M1) used stepwise selection in which the Akaike information criterion (AIC) is used to compare predictive diagnostic models with different variables. The second method (M2) incorporated age and gender as covariates in addition to the stepwise selection methodology. The third selection method (M3) used linear discriminant analysis (LDA) and LDA Effect Size (LEfSe) algorithms for strain marker discovery. A fourth method (M4) used LEfSe to incorporate age and sex as covariates in addition to incorporating selected strain markers. In addition, the fifth method (M5) was calculated by a machine learning algorithm based on the Gradient Boosting machine (GBM) ensemble method.

GBM was integrated into modeling using scikit-learn's Gradient Boosting Regressor (version 0.21.3) in Python (version 3.6.9). After variable selection, predictive diagnostic models were calculated using logistic regression with training and test sets set to an 80:20 ratio for model validation.

Experimental Example 6. Statistical Analysis

Significant age differences between BT and control groups were determined by Student's t-test and Wilcoxon ranksum test, respectively. A chi-square test was performed to determine statistical differences between groups based on the sex of the cohort. Praycipal coordinate analysis (PCoA) was performed to determine the individual classification level clustering of groups according to Bray-Curtis dissimilarity distance. To analyze the difference in microbiome composition between the HC group and the BT group, a Student's t-test was performed. LEfSe was used to determine significant, differentiated abundant genera between clinical groups, for the selection of biomarkers of statistical and biological significance. The LEfSe algorithm set the cutoff LDA score (log10) to 2 using Wilcoxon Ranksum test and Linear Discriminate Analysis (LDA). Results were considered significant when p values were less than 0.05 (p <0.05), and all analyzes were performed using R version 3.6.1.

Example 1. Clinical characteristics

Through evaluation of the clinical characteristics of HC (Healthy control) and BT (Brain tumor) subject groups, it was determined that there was a significant difference between the two groups (p <0.001). HC subjects ranged from 40 to 78 years old, with a mean age of 59.7 (SD 10.5), and BT subjects 16 to 81 years old, with a mean age of 51.5 (SD 14.2) (Table 2).

Example 2. Diversity

In the BT group, the Chao1 index of species abundance and the Shannon index of bacterial community diversity were significantly higher, but the difference in Simpson's index between the BT group and the control group was not significant (Fig. 1a).

PCoA was performed and all samples were plotted along the two most dissimilar principal coordinates (PCo, principal coordinates) among the samples to evaluate the similarity between the HC and BT groups. At all taxa levels, significant clustering was observed between the two groups (p <0.001) (Fig. 1b).

Example 3. Identification of microbial EV abundance and biomarkers in serum

At the phylum level, Firmicutes abundance was significantly lower in the control group than in the patient group, whereas Actinobacteria and Proteobacteria were higher (Figures 2a and 2c). LEfSe analysis of phylum-level biomarkers led to Actinobacteria, Proteobacteria and Firmicutes as the only phyla with log (LDA score) values greater than 4 (FIG. 3a).

At the class level, there were significant changes in Clostridia, Bacilli, Erysipelotrichia, Gammaproteobacteria, Actinobacteria, Alphaproteobacteria and Negativicutes. Eight strong-level biomarkers were determined using Actinobacteria yielding a log (LDA score) of 4.0, which showed a biologically significantly higher abundance in the control group (Fig. 4a).

At the order level, Clostridiales, Bifidobacteriales, Erysipelotrichales, Lactobacillales, Micrococcales, Sphingomonadales and Selenomonadales were significantly changed between control and patient groups. As a result of LEfSe evaluation at the neck level, 13 taxa were significantly different between the control group and the patient group, and Clostridiales showed the greatest difference with a log (LDA score) value of 3.9 (Fig. 4b).

At the family level, Ruminococcaceae, Lactobacillaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, Streptococcaceae, Sphingomonadaceae and Porphyromonadaceae were significantly changed. As a result of LEfSe evaluation at the family level, a total of 21 taxa were significantly different, and Ruminococcaceae had a log (LDA score) value greater than 4.0 (Fig. 4c).

Finally, as a result of analysis at the genus level, taxa showing a number of significant differences between the control group and the BT group were determined (Fig. 2b). Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Lactobacillus, Ruminiclostridium 6 and Peptoclostridium were significantly higher in the BT group compared to the control group, whereas [Eubacterium] coprostanoligenes, Streptingocomocus, bacter, Blautingocomonas, Blauting significantly lower levels (Fig. 2d).

LEfSe analysis of genus-level serum EV microbiome composition yielded a total of 30 genera, in particular Ruminococcaceae UCG-014 was found to exceed 4.0 (Fig. 3b). A total of 4, 9, 12, 18, and 29 taxa showed a ratio higher than 0.5% in all groups and showed a significant difference between the control group and the patient group using the t-test in the phyla, the class, the order, and the family (p). <0.05).

Example 4. Development of a Serum Microbial EV Metagenome-Based Brain Tumor (BT) Diagnostic Model

Using the serum microbial EV metagenomic profiles of the control and BT groups, a diagnostic model was developed to determine the risk of brain tumors in healthy subjects.

Following stepwise selection and logistic regression analysis, the M1 and M2 models yielded 12 important microbial EV genera, Stenotrophomonas, Knoellia, Sphingomonas, Solanum melongena, Parabacteroides, Actinomyces, Ruminiclostridium, Lactococcus, Turicibacter, Faecalibacterium, Streptococcus, and Bifidobacterium.

On the other hand, logistic regression analysis using biomarkers determined by LEfSe analysis for M3 and M4 showed that 29 different important microbial EV genera, Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Lactobacillus, Lachnospiraceae(f), Acinetobacter, Staphylococcus, Pseudomonas, Ruminococcaceae UCG-013, Klebsiella, Bifidobacterium, Ruminococcus 1, Streptococcus, Ruminiclostridium 6, Peptoclostridium, Sphingomonas, Clostridiales vadinBB60(f), Turicibacter, Ruminococcoraceae(f), Ruminococcus 2, Corynebacterium, Proynebacterium, Proynebacterium 1, Proynebacterium Solanum melongena, Actinomyces, Knoellia, Stenotrophomonas, and Veillonella were produced.

In the case of M5, the relative abundance of the analyzed total microbial EV metagenomic information was entered as a feature for analysis rather than a specific biomarker.

Model performance using the test set was evaluated based on the AUC, sensitivity, specificity, and accuracy of each method to determine the optimal BT diagnostic model. As a result, all of the BT diagnostic models showed AUC higher than 0.93 (Fig. 5a).

The model based on the GBM method showed the highest sensitivity, specificity and AUC with values of 1.000, 0.936 and 0.993, respectively (Fig. 5b).

Example 5. Development of Brain Tissue Microbial EV Metagenome-Based Brain Tumor (BT) Diagnostic Model

At the phylum level, Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria were the most abundant phyla in all groups, accounting for over 80% of tissue EV taxa in patients and controls. Cyanobacteria and Saccharibacteria were significantly higher in the control group than in the patient group ( FIGS. 6a and 6c ).

At the class level, Erysipelotrichia was significantly lower in the control group than in the patient group. On the other hand, Clostridia, Saccharibacteria (p) and Chloroplast were significantly higher in the control group (Fig. 7a).

At the order level, Clostridiales, Chloroplast (c) and Saccharibacteria (p) were significantly abundant in the control group, whereas Erysipelotrichales were significantly lower (Fig. 7b).

At the family level, Bacteroidaceae, Ruminococcaceae, Bacteroidales S24-7 group, Erysipelotrichaceae, Chloroplast (c), Prevotellaceae and Saccharibacteria (p) significantly changed between clinical groups, and the biomarkers discovered through LEfSe included the Bacteroidales S24-7 group. , Ruminococcaceae, Prevotellaceae, Bacteroidaceae and Erysipelotrichaceae were included (Fig. 7c).

At the genus level, Bacteroides and Erysipelatoclostridium were significantly lower in the control group than in the BT group, whereas the Bacteroidales S24-7 group, Chloroplast (c), Lachnospiraceae NK4A136 group, Prevotella 9, and Candidatus Saccharimonas were significantly higher (Fig. 6b, Fig. 6d). LEfSe evaluation at the genus level revealed the Bacteroidales S24-7 group, Lachnospiraceae NK4A136, Bacteroides and Erysipelatoclostridium as important brain tumor biomarkers ( FIG. 6E ).

In addition, to compare changes in the microbiome composition of serum and tissues between control and patients, fold changes in microbial EV composition of serum and tissues obtained from the same individual were analyzed.

At the phylum level, the patient's Saccaribacteria were significantly reduced in both serum and tissue ( FIG. 8a ).

At the class level, patients' Erysipelotrichia were significantly increased in both serum and tissue samples ( FIG. 8B ).

At the order level, Erysipelotrichiales were significantly increased in both patient serum and tissue samples ( FIG. 8c ).

At the family level, Erysipelotrichiales were significantly higher in serum and tissues of patients, and Prevotellaceae were significantly reduced ( FIG. 8d ).

Finally, at the genus level, patients' [Eubacterium] rectale (E. rectale) and Dialister were significantly reduced in both serum and tissue samples. In addition, it was confirmed that Lachnospiraceae NK4A136 was significantly lower in the brain tumor patient tissue, but significantly increased in the patient's serum compared to the control group and serum samples, respectively (FIG. 9).

Example 6. Development of a brain tumor diagnosis model using a blood sample

Example 6-1. Metagenomic analysis of DNA extracted from blood

In order to isolate vesicles present in blood and extract DNA, first put blood in a 10 ml tube and centrifuge (3,500 x g, 10 min, 4 ° C) to settle the suspension to recover only the supernatant, and then to a new 10 ml tube. moved After removing bacteria and foreign substances from the recovered supernatant using a 0.22 μm filter, transfer to a centripreigugal tube (centripreigugal filters 50 kD) and centrifuge at 1500 x g, 4°C for 15 minutes, discarding substances smaller than 50 kD and 10 ㎖ concentrated until After removing bacteria and foreign substances using a 0.22 μm filter once again, discard the supernatant using a high-speed centrifugation method at 150,000 x g, 4°C for 3 hours with a Type 90ti rotor, and dissolve the lumped pellet with physiological saline (PBS). A vesicle was obtained.

100 μl of vesicles isolated from blood according to the above method was boiled at 100° C. to release DNA from the inside out of lipids, and then cooled on ice for 5 minutes. Next, in order to remove the remaining suspended matter, centrifugation was performed at 10,000 x g, 4°C for 30 minutes, and only the supernatant was collected and the amount of DNA was quantified using Nanodrop. Thereafter, PCR was performed with the 16s rDNA primer shown in Table 1 to confirm whether bacterial-derived DNA was present in the extracted DNA, and it was confirmed that the bacterial-derived gene was present in the extracted gene. Output the result as a Standard Flowgram Format (SFF) file, convert the SFF file into a sequence file (.fasta) and nucleotide quality score file using GS FLX software (v2.9), then check the credit rating of the read, window (20 bps) The part with an average base call accuracy of less than 99% (Phred score <20) was removed. After removing the low-quality part, only reads with a length of 300 bps or more were used (Sickle version 1.33). For the analysis of the results, the Operational Taxonomy Unit (OTU) performed clustering according to the sequence similarity using UCLUST and USEARCH. Specifically, clustering is performed based on sequence similarity of 94% for genus, 90% for family, 85% for order, 80% for class, and 75% for phylum. Classification of OTU phyla, class, order, family, and genus level was performed, and bacteria having a sequence similarity of 97% or more were analyzed using BLASTN and GreenGenes' 16S DNA sequence database (108,453 sequences) (QIIME).

Example 6-2. Brain tumor diagnosis model based on metagenome analysis of bacteria-derived vesicles isolated from blood

By the method of Example 6-1, metagenome sequencing was performed after vesicles were isolated from the blood of 170 brain tumor patients and 200 normal people matched with age and gender. To develop a diagnostic model, first, in the t-test, strains with a p-value of 0.05 or less between the two groups and a group average of 0.1% or more are selected, and then, the diagnostic performance indicator AUC (area under curve), Sensitivity, and specificity were calculated.

As a result of analyzing bacterial-derived vesicles in blood at the phylum level, when a diagnostic model was developed with bacterial biomarkers of Actinobacteria and Tenericutes, the diagnostic performance for brain tumors was significantly (see Table 3 and FIG. 10). .

control brain tumor t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity p__Actinobacteria 0.0625 0.0492 0.0316 0.0210 0.0000 0.51 0.75 0.70 0.70 0.71 p__Tenericutes 0.0018 0.0039 0.0033 0.0026 0.0000 1.86 0.72 0.64 0.85 0.46

As a result of analyzing the bacterial-derived vesicles in the blood at the class level, when a diagnostic model was developed with the Actinobacteria and Mollicutes class bacterial biomarkers, the diagnostic performance for brain tumors was significantly (see Table 4 and Fig. 11). .

control brain tumor t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity c__Actinobacteria 0.0542 0.0474 0.0258 0.0204 0.0000 0.48 0.73 0.70 0.67 0.73 c__Mollicutes 0.0018 0.0039 0.0033 0.0026 0.0000 1.86 0.72 0.64 0.85 0.46

As a result of analyzing the bacterial-derived vesicles in the blood at the order level, when a diagnostic model was developed with the Turicibacterales, Xanthomonadales, and RF39 order bacterial biomarkers, the diagnostic performance for brain tumors was significant (Table 5 and Fig. 12) Reference).

control brain tumor t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity o__Turicibacterales 0.0042 0.0060 0.0090 0.0055 0.0000 2.15 0.76 0.59 0.76 0.46 o__Xanthomonadales 0.0076 0.0123 0.0009 0.0017 0.0000 0.12 0.73 0.74 0.64 0.83 o__RF39 0.0013 0.0027 0.0026 0.0022 0.0000 2.07 0.70 0.59 0.85 0.39

As a result of analyzing bacterial-derived vesicles in the blood at the family level, when a diagnostic model was developed with bacterial biomarkers of Intrasporangiaceae, Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nocardiaceae, Xanthomonadaceae, Weeksellaceae, and Veillonellaceae, the diagnostic performance for brain tumors was significantly (see Table 6 and Figure 13).

control brain tumor t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity f__Intrasporangiaceae 0.0075 0.0278 0.0002 0.0006 0.0003 0.03 0.77 0.74 0.48 0.95 f__Lactobacillaceae 0.0263 0.0217 0.0516 0.0228 0.0000 1.97 0.76 0.62 0.73 0.54 f__[Mogibacteriaceae] 0.0005 0.0017 0.0010 0.0021 0.0157 1.96 0.76 0.51 0.94 0.17 f__Turicibacteraceae 0.0042 0.0060 0.0090 0.0055 0.0000 2.15 0.76 0.59 0.76 0.46 f__Nocardiaceae 0.0008 0.0024 0.0014 0.0023 0.0206 1.69 0.74 0.49 1.00 0.07 f__Xanthomonadaceae 0.0075 0.0119 0.0009 0.0017 0.0000 0.12 0.73 0.77 0.67 0.85 f__[Weekselaceae] 0.0025 0.0047 0.0038 0.0054 0.0137 1.53 0.71 0.50 1.00 0.10 f__Veillonellaceae 0.0115 0.0154 0.0045 0.0106 0.0000 0.39 0.71 0.58 0.73 0.46

As a result of analyzing bacterial-derived vesicles in blood at the genus level, when a diagnostic model was developed with bacterial biomarkers of Stenotrophomonas, Lactobacillus, Turicibacter, Tepidimonas, Sphingomonas, Rhodococcus, and Cloacibacterium, the diagnostic performance for brain tumors was significant. (see Table 7 and Figure 14).

control brain tumor t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity g__Stenotrophomonas 0.0060 0.0111 0.0001 0.0003 0.0000 0.01 0.79 0.78 0.64 0.90 g__Lactobacillus 0.0261 0.0216 0.0514 0.0228 0.0000 1.97 0.76 0.62 0.73 0.54 g__Turicibacter 0.0042 0.0060 0.0090 0.0055 0.0000 2.15 0.76 0.59 0.76 0.46 g__Tepidimonas 0.0002 0.0009 0.0011 0.0024 0.0000 4.79 0.76 0.49 0.91 0.15 g__Sphingomonas 0.0116 0.0440 0.0015 0.0044 0.0016 0.13 0.75 0.65 0.55 0.73 g__Rhodococcus 0.0008 0.0024 0.0014 0.0024 0.0220 1.69 0.74 0.49 1.00 0.07 g__Cloacibacterium 0.0019 0.0040 0.0034 0.0052 0.0030 1.76 0.74 0.51 0.94 0.17

Table 8 below briefly shows the results of analysis of bacterial vesicles in serum, blood and brain tissue samples.

Increase/decrease in brain tumor patients compared to normal controls increase decrease serum door Firmicutes Actinobacteria
Proteobacteria River Clostridia,
Bacill
Erysipelotrichia Gammaproteobacteria
Actinobacteria
Alphaproteobacteria
Negativicutes neck Clostridiales
Bifidobacteriales
Erysipelotrichales Lactobacillales
Micrococcales
Spingomonadales
Selenomonadales class Ruminococcaceae
Lactobacillaceae
Peptostreptococccaceae
Erysipelotrichaceae Lachnospiraceae
Streptococcaceae
Bifidobacteriaceae
Sphingomonadaceae
Porphyromonadaceae inside Ruminococcaceae UCG-014
Lachnospiraceae NK4A136
Ruminococcaceae UCG-013
Lactobacillus
Ruminiclostridium 6
Peptoclostridium [Eubacterium] coprostanoligenes
Escherichia-Shigella
Blautia
Bifidobacterium
Streptococcus
Sphinomonas Organization door Firmicutes
Bacteroidetes
Actinobacteria
Proteobacteria Cyanobacteria
Saccaribacteria River Erysipelotrichia Clostridia
Saccaribacteria (p)
Chloroplast neck Erysipelotrichales Clostridiales
Chloroplast (c)
Saccaribacteria (p) class Bacteroidaceae
Erysipelotrichaceae Ruminococcaceae
Bacteroidales S24-7 group
Chloroplast (c)
Prevotellaceae
Saccaribacteria (p) inside Bacteroides
Erysipelatoclostridium Bacteroidales S24-7 group
Chloroplast (c)
Lachnospiraceae NK4A136 family
Prevotella 9
Candidatus Saccharimonas blood door Tenericutes Actinobacteria River Mollicutes Actinobacteria neck Turicibacterales Xanthomonadales class Lactobacillaceae
Mogibacteriaceae
Turicibacteraceae
Nocardiaceae
Weeksellaceae Intrasporangiaceae
Xanthomonadaceae
Veillonellaceae
inside Lactobacillus
Turicibacter
Tepidimonas
Rhodococcus
Cloacibacterium Stenotrophomonas
Sphinomonas

The description of the present invention stated above is for illustration, and those of ordinary skill in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. There will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

<110> MD Healthcare Inc. <120> Method for diagnosis of brain tumor using analysis of bacteria metagenome <130> MP19-158P1 <150> KR 10-2019-0082317 <151> 2019-07-08 <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 (10)

(a) extracting DNA from extracellular vesicles isolated from normal and subject samples;
(b) performing a polymerase chain reaction (PCR) on the extracted DNA using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2; and
(c) a method for providing information for brain tumor diagnosis, comprising comparing the increase or decrease in the content of a normal human-derived sample and a bacterial-derived extracellular vesicle through sequencing of the PCR product,
The normal and subject samples are blood or serum;
When the sample of the normal person and the subject is blood,
In the step (c), the content of vesicles derived from normal human samples and Lactobacillus, Rhodococcus, Sphingomonas, and Stenotrophomonas genus bacteria-derived vesicles increase or decrease comparing; or
When the normal and subject samples are serum,
In step (c), the content of vesicles derived from normal human samples and vesicles derived from Knoellia, Stenotrophomonas, Lactococcus, and Turicibacter genus bacteria Information providing method for brain tumor diagnosis, characterized in that it comprises the step of comparing the.
According to claim 1,
When the sample of the normal person and the subject is blood,
In the step (c), the content of vesicles derived from normal human samples and Lactobacillus, Rhodococcus, Sphingomonas, and Stenotrophomonas genus bacteria-derived vesicles increase or decrease compare;
Extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of Actinobacteria and Tenericutes;
One or more class bacteria-derived extracellular vesicles selected from the group consisting of Actinobacteria and Mollicutes;
Extracellular vesicles derived from one or more order bacteria selected from the group consisting of Turicibacterales and Xanthomonadales,
Intrasporangiaceae, Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nocardiaceae, Xanthomonasae (Xanthomonadaceae), Weeksellaceae, and Veillonellaceae, extracellular vesicles derived from one or more family bacteria selected from the group consisting of, or
To diagnose brain tumors by comparing the increase or decrease in the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of Turicibacter, Tepidimonas, and Cloacibacterium. A method for providing information for brain tumor diagnosis, characterized in that.
According to claim 1,
When the normal and subject samples are serum,
In step (c), the content of vesicles derived from normal human samples and vesicles derived from Knoellia, Stenotrophomonas, Lactococcus, and Turicibacter genus bacteria compare;
Extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of Firmicutes, Actinobacteria, and Proteobacteria;
Clostridia, Bacill, Erysipelotrichia, Gammaproteobacteria, Actinobacteria, Alphaproteobacteria, and Negatibicoccus One or more class bacteria-derived extracellular vesicles selected from the group consisting of Negativicutes,
Clostridiales, Bifidobacteriales, Erysipelotrichales, Lactobacillales, Micrococcales, Sphingomonadales, and Selenomonas One or more orders (order) bacteria-derived extracellular vesicles selected from the group consisting of the order (Selenomonadales),
Ruminococcaceae, Lactobacillaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, Streptococcus phingae, Streptococcus Extracellular vesicles derived from one or more family bacteria selected from the group consisting of Sphingomonadaceae, and Porphyromonadaceae, or
Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Lactobacillus, Ruminiclostridium 6, Peptoclostridium 6 (Peptoclostridium), Eubacterium coprostanoligenes ([Eubacterium] coprostanoligenes), Escherichia-Shigella, Blautia, Bifidobacterium, Streptococcus, s Phingomonas, Solanum melongena, Parabacteroides, Actinomyces, Ruminiclostridium, Faecalibacterium, Lacnospirae Lachnospiraceae (f), Acinetobacter, Staphylococcus, Pseudomonas, Klebsiella, Ruminococcus 1, Salmonella, Clostridial Clostridiales vadinBB60 (f), Ruminococcaceae (f), Ruminococcus 2, Peptococcaceae (f), Diaphorobacter, Coryne Bacterium (Corynebacterium) 1, Propionibacterium (Propionibacterium), and Veillonella (Veillonella) to diagnose brain tumor by comparing the increase or decrease in the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of A method for providing information for brain tumor diagnosis, characterized in that.
According to claim 1,
The blood is whole blood, plasma, or blood mononuclear cells, characterized in that, information providing method for brain tumor diagnosis. delete delete delete delete delete delete

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