KR20210006282A - Method for diagnosis of brain tumor using analysis of bacteria metagenome - Google Patents

Abstract

The present invention relates to a method for diagnosing brain tumor through a bacterial metagenome analysis. More specifically, the present invention relates to a method for diagnosing brain tumor by analyzing an increase or a decrease in the content of specific bacteria-derived extracellular vesicles by performing a bacterial metagenome analysis using samples of normal individuals and tested subjects. Extracellular vesicles secreted by bacteria present in the environment are absorbed into a body and may directly affect the occurrence of inflammation. Also, it is difficult to diagnose brain tumor early before symptoms appear, and thus it is difficult to perform effective treatment. Accordingly, according to the present invention, by diagnosing the risk of brain tumor in advance through the metagenome analysis of bacteria-derived extracellular vesicles using human body-derived samples, it is possible to diagnose and predict the risk group of autism early. In addition, proper management can delay or prevent the onset of disease. Moreover, an early stage diagnosis is possible even after the onset of the diseases, and thus the occurrence rate of brain tumor is reduced. Also, a therapeutic effect can be improved. Furthermore, by avoiding exposure to causative factors through a metagenome analysis in a patient diagnosed with brain tumor, the course of diseases can be improved, or recurrence can be prevented.

Description

[Method for diagnosis of brain tumor using analysis of bacteria metagenome}

The present invention relates to a method for diagnosing brain tumors through bacterial metagenomic analysis, and more specifically, by performing a bacterial metagenomic analysis using samples derived from normal individuals and subjects, and analyzing the increase or decrease of the content of extracellular vesicles derived from specific bacteria. It is about how to diagnose.

Brain tumors are tumors that occur in the brain and central nervous system and can be used interchangeably with "brain cancer", and are the fourth and fifth most common systemic tumors, especially in children. It is the second most frequent tumor among tumors. The incidence rate of brain tumors is about 10 per 100,000 population, and among primary brain tumors, the most common glioma accounts for about 35% of the total and is malignant. As one of the causes of brain tumors, mutagenic substances known so far include radiation, chemicals, and viruses, and it is known that the incidence of brain tumors increases even when the immune function decreases, such as AIDS. Recently, interest in the relationship between chemical substances and cancer incidence is increasing due to environmental pollution, but there is no clear causal relationship between the occurrence of brain tumors.

The mechanisms by which brain tumors cause clinical symptoms are symptoms due to increased brain pressure, symptoms caused by pressure on the surrounding brain by brain tumors, symptoms caused by electrical stimulation of the surrounding cranial nerves by brain tumors (epileptic seizures), and symptoms due to hormonal abnormalities. Included. Without active treatment, both malignant and benign brain tumors have fatal consequences.

As a method of determining the prognosis of existing brain tumor patients, it is common to synthesize the progress of clinical treatment, radiation therapy, and chemotherapy, and determine the survival prognosis including the possibility of recurrence by considering the malignancy, location, and age of the brain tumor. Although it was a method, it cannot be said to be an accurate prognostic method because there are various differences for each type of patient and results for clinical treatment are also different. Therefore, the necessity of 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, microbiota or microbiome refers to a microbial community including bacteria, archaea, and eukarya existing in a given dwelling place, and the intestinal microbiota refers to human physiological phenomena. It plays an important role and is known to have a great effect on human health and disease through interaction with human cells. Bacteria living 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 through which particles larger than 200 nanometers (nm) cannot pass, and bacteria that coexist in the mucous membrane cannot pass through the mucous membrane, but vesicles derived from bacteria are usually less than 100 nanometers in size. It freely passes through the mucous membrane and is absorbed by our body.

Metagenomics, also called environmental genomics, can be said to be the analysis of metagenomic data obtained from samples taken from the environment. Recently, it has become possible to list the bacterial composition of the human microbiota by a method based on the 16s ribosomal RNA (16s rRNA) sequence, and the 16s rDNA sequence, the gene of the 16s ribosomal RNA, has been subjected to next generation sequencing. , NGS) platform. However, in the onset of brain tumors, there has been no report on a method of identifying the causative factor of brain tumors and predicting brain tumors through metagenomic analysis present in vesicles derived from bacteria in human-derived products such as blood.

Korean 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 the causative factor and risk of developing brain tumors in advance, and performed metagenomic analysis on this, and 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 a method of providing information for diagnosing brain tumors through metagenomic analysis of extracellular vesicles derived from bacteria, a method for diagnosing brain tumors, and a method for predicting the risk of developing brain tumors.

However, the technical problem to be achieved by the present invention is not limited to the above-mentioned problems, and other problems that are 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 diagnosis of brain tumor, 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 a primer pair of SEQ ID NO: 1 and SEQ ID NO: 2; And

(c) comparing the increase or decrease in the content of the sample derived from a normal person and extracellular vesicles derived from bacteria through the sequence analysis of the PCR product.

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

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

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

(c) comparing the increase or decrease in the content of the sample derived from a normal person and extracellular vesicles derived from bacteria through the sequence analysis of the PCR product.

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

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

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

(c) comparing the increase or decrease in the content of the sample derived from a normal person and extracellular vesicles derived from bacteria through the sequence analysis 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), Clostridia, Basil, Erysipelotrichiam, Gammaproteobacteria, and alpha proteobacteria ( Alphaproteobacteria), Negativicutes, Saccharibacteria (p)), Chloroplast, Actinobacteria and Molycutes 1 selected from the group consisting of It is possible to compare the increase or decrease of the content of extracellular vesicles derived from class bacteria or more.

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 It is possible to compare the increase or decrease in the content of extracellular vesicles derived from one or more order bacteria selected from the group consisting of Xanthomonadales.

In another embodiment of the present invention, in the step (c), Luminococcaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, Streptococcus Streptococcaceae, Bifidobacteriaceae, Sphingomonadaceae, Porphyromonadaceae, Bacteroidaceae, Bacteroidales S24-7 (Bacteroidales S24-7), Chloro Chloroplast (c)), Prevotellaceae, Saccharibacteria (p)), Intrasporangiaceae, Lactobacillaceae, Mojibacteria ( Mogibacteriaceae), Turicibacteraceae, Nocardiaceae, Xanthomonadaceae, Weeksellaceae, and Veillonellaceae from the group consisting of It is possible to compare the increase or decrease in the content of extracellular vesicles derived from one or more selected family bacteria.

In another embodiment of the present invention, in the step (c), Luminococcaceae UCG-014, Lachnospiraceae NK4A136, Luminococcaceae UCG-013, Luminiclostridium (Ruminiclostridium) 6, Peptoclostridium, Eubacterium coprostanoligenes, Escherichia-Shigella, Blautia, Bifidobacterium ), Streptococcus, Bacteroides, Erysipelatoclostridium, Bacteroidales S24-7, Chloroplast (c)), Prevotella (Prevotella) 9, Candidatus Saccharimonas, Stenotrophomonas, Lactobacillus, Turicibacter, Tepidimonas, Sphingomonas , Rhodococcus, and Cloacibacterium (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, and Saccharibacteria ), and one or more phylum bacteria-derived extracellular vesicles selected from the group consisting of Tenericutes,

Clostridia, Basil, Erysipelotrichiam, Gammaproteobacteria, Alphaproteobacteria, Negativicutes, Saccharii Extracellular vesicles derived from one or more class bacteria selected from the group consisting of Saccharibacteria (p), Chloroplast, Actinobacteria, and Molycutes,

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

Luminococcaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, Streptococcaceae, Streptococcaceae, Bifidobacteriaceae, Bifidobacteriaceae, Bifidobacteriaceae Spingomonadaceae, Prophyromonadaceae, Bacteroidaceae, Bacteroidales S24-7, Chloroplast (c), Prevotellaceae ), Saccharibacteria (p), Intrasporangiaceae, Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nokar Extracellular vesicles derived from one or more family bacteria selected from the group consisting of Nocardiaceae, Xanthomonadaceae, Weeksellaceae, and Veillonellaceae, or

Luminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Ruminiclostridium 6, Peptoclostridium, Eubacterium Coprostanoligenes ([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 It is possible to compare the increase or decrease in 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), compared with a sample derived from a normal person,

Tenericutes (Tenericutes) phylum bacteria-derived extracellular vesicles,

Extracellular vesicles derived from Mollicutes class bacteria,

Extracellular vesicles derived from the order bacteria of Turicibacterales,

At least one selected from the group consisting of Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nocardiaceae, and Weeksellaceae. 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 outer vesicles 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 Actinobacteria phylum bacteria,

Extracellular vesicles derived from Actinobacteria class bacteria,

Xanthomonadales (Xanthomonadales) order bacteria-derived extracellular vesicles,

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

If the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of Stenotrophomonas and 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 Firmicutes,

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

Extracellular vesicles derived from one or more order bacteria selected from the group consisting of Clostridiales, Bifidobacteriales, and Erysipelotrichales,

Extracellular derived from one or more family bacteria selected from the group consisting of Luminococcaceae, Lactobacillaceae, Peptostreptococcaceae, and Erysipelotrichaceae. Parcel, or

Luminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Lactobacillus, Ruminiclostridium 6, Peptoclostridium If the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of (Peptoclostridium) 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 one or more phylum bacteria selected from the group consisting of Actinobacteria and Proteobacteria,

Extracellular vesicles derived from one or more class bacteria selected from the group consisting of Gammaproteobacteria, Actinobacteria, Alphaproteobacteria, and Negativicutes,

Extracellular vesicles derived from one or more order bacteria selected from the group consisting of Lactobacillales, Micrococcales, Sphingomonadales, and Selenomonadales,

One or more families selected from the group consisting of Lachnospiraceae, Streptococcaceae, Bifidobacteriaceae, Sphingomonadaceae, and Porphyromonadaceae ( family) extracellular vesicles derived from bacteria, or

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

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,

Extracellular vesicles derived from bacteria of the order Erysipelotrichales,

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, 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 Cyanobacteria, and Saccharibacteria,

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

Extracellular vesicles derived from one or more order bacteria selected from the group consisting of the order Clostridiales, the order Chloroplast (c), and the order Saccharibacteria (p),

Luminococcaceae, 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

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

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

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

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

Extracellular vesicles secreted from bacteria existing in the environment are absorbed into the body and can have a direct effect on the occurrence of inflammation. Brain tumors are difficult to treat efficiently because early diagnosis is difficult before symptoms appear. Accordingly, by pre-diagnosing the risk of developing brain tumors through metagenomic analysis of bacterial-derived extracellular vesicles using human samples according to the present invention, it is possible to diagnose and predict the risk group of autism early, and also to determine the onset time through appropriate management. You can slow it down or prevent it from occurring. In addition, early diagnosis can be performed even after the onset, which not only lowers the incidence of brain tumors and increases the treatment effect, but also improves the course of the disease or prevents recurrence by avoiding exposure of causative factors through metagenomic analysis in patients diagnosed with brain tumors. There is an advantage that can be prevented.

1A and 1B show (a) alpha diversity of serum EV microbiome in healthy control group (HC) and brain tumor patients (BT) using PCoA based on Bray-Curtis similarity at the genus level (Chao1, Shannon and Simpson index) and (b) beta diversity.
2A to 2D show differences in microbiome abundance between healthy control group (HC) and brain tumor patients (BT) in serum at the level of the phylum and genus. (a) shows the abundance heat map at the level of the serum microbial EV, (b) shows the heat map of the abundance at the genus level of the serum microbial EV, and (c) shows the major microbial EV by t-test. At the door level, (d) is at the inner level.
3A and 3B show significantly different microbial EVs determined through LEFSe analysis at phylum level (a) and genus level (b).
Figures 4a to 4c show the abundance of microbiome at (a) class, (b) order, (c) and (family) levels in serum.
5A and 5B show a brain tumor diagnosis model based on 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 curve for M5.
6A to 6E show differences in microbiome abundance between the healthy control group (HC) and brain tumor patients (BT) in brain tissue at the level of the phylum and genus, (a) and (b), respectively, And heat maps of the EV taxa of key microorganisms in brain tissue at the genus level, (c) and (d) are the major microbial EVs by t-test at the phylum and genus level, respectively, and (e) are significantly different determined through LEfSe analysis. Microbial EV biomarkers are shown at the genus level.
7a to 7c show the abundance of microbiome at the level of (a) class, (b) order, (c) and (family) in brain tissue.
Figures 8a to 8d show the fold-change between the healthy control group (HC) and brain tumor patients (BT) of serum and tissues at (a) door, (b) river, (c) neck, (d) and level will be. Red indicates the genera of microbial EVs that are significantly different between clinical groups in serum, green indicates the genera of microbial EVs that are significantly different between clinical groups in brain tissue, and blue indicates significant differences in both serum and tissue samples. It shows the genus of the microorganism EV.
9 shows the fold-change between the healthy control group (HC) and brain tumor patients (BT) of serum and tissue at the genus level. Red indicates the genera of microbial EVs that are significantly different between clinical groups in serum, green indicates the genera of microbial EVs that are significantly different between clinical groups in brain tissue, and blue indicates significant differences in both serum and tissue samples. It shows the genus of the microorganism EV.
10 is a result showing the distribution of bacterial-derived vesicles (EVs) having significant diagnostic performance at the phylum level by performing metagenomic analysis after separating bacterial vesicles from brain tumor patients and normal human blood.
11 is a result showing the distribution of bacterial-derived vesicles (EVs) with significant diagnostic performance at a class level by separating bacterial-derived vesicles from blood of brain tumor patients and normal humans, and then performing metagenomic analysis.
12 is a result showing the distribution of bacterial-derived vesicles (EVs) having significant diagnostic performance at the order level by separating bacterial-derived vesicles from blood of brain tumor patients and normal humans, and then performing metagenomic analysis.
13 is a result showing the distribution of bacterial-derived vesicles (EVs) having significant diagnostic performance at the family level by separating bacterial-derived vesicles from the blood of brain tumor patients and normal humans, and then performing metagenomic analysis.
14 is a result showing the distribution of bacterial-derived vesicles (EVs) having significant diagnostic performance at the genus level by separating bacterial-derived vesicles from the blood of brain tumor patients and normal humans, and then performing metagenomic analysis.

The present invention relates to a method for diagnosing brain tumors through bacterial metagenomic analysis, wherein the present inventors extracted genes from extracellular vesicles derived from bacteria using samples derived from normal individuals and subjects, and performed metagenomic 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 on the extracted DNA using a primer pair of SEQ ID NO: 1 and SEQ ID NO: 2; And

(c) It provides an information providing method for diagnosing a brain tumor comprising the step of comparing the increase or decrease in the content of a sample derived from a normal person and an extracellular vesicle derived from bacteria through the sequence analysis of the PCR product.

The term "brain tumor diagnosis" used in the present invention means to determine whether a patient is likely to develop a brain tumor, 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 period or prevent onset through special and appropriate management as a patient with a high risk of developing brain tumors in any specific patient. In addition, the method of the present invention can be used clinically to determine a treatment by early diagnosis of a brain tumor and selecting the most appropriate treatment method.

The term "metagenome" used in the present invention is also referred to as "military genome", 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 describe the identification of many microorganisms at once using a sequence analyzer to analyze microorganisms that cannot be cultured. In particular, metagenome does not refer to the genome or genome of one species, but refers to a kind of mixed genome as the genome of all species in one environmental unit. This is a term that came from the point of view that not only one existing species functionally but also various species interact with each other to create a complete species when defining a species in the course of the development of biology in an ohmic way. Technically, it is the subject of a technique that uses rapid sequencing to analyze all DNA and RNA regardless of species, to identify all species within one environment, and to identify interactions and metabolism. In the present invention, metagenomic analysis was preferably performed using extracellular vesicles derived from bacteria isolated from serum.

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

In an embodiment of the present invention, metagenomic analysis was performed on the bacterial-derived extracellular vesicles, and analyzed at the level of phylum, class, order, family, and genus, respectively. Thus, vesicles derived from bacteria that can actually act as the cause of brain tumor development were identified (see Table 8 of the present invention).

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

At the level of the phylum, Firmicutes, Actinobacteria, Proteobacteria,

At the river (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 the genus level, Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Lactobacillus, Ruminiclostridium 6, Peptoclostridium, [Eubacterium] coprostanoligenes, Escherichia-Shigella, Blautia, Bifidobacterium, Streptococcus

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

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

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

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

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

At the family level, Bacteroidaceae, Erysipelotrichaceae, Ruminococcaceae, Bacteroidales S24-7 group, 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 bacterial-derived extracellular vesicles between brain tumor patients and normal subjects (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 is There was a significant difference between brain tumor patients and normal subjects (see Example 6).

More specifically, in one embodiment of the present invention, as a result of analyzing the bacterial metagenome on the vesicle present in the blood sample at a class level, the content of the extracellular vesicles derived from Actinobacteria and Mollicutes class bacteria is a brain tumor patient. There was a significant difference between the and normal subjects (see Example 6).

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

Through the results of the above examples, it was confirmed that the distribution variables of the identified bacterial-derived extracellular vesicles can be usefully used in predicting the occurrence of brain tumors.

Hereinafter, a preferred embodiment is presented to aid the understanding of the present invention. However, the following examples are 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 from a total of 182 brain tumor (BT) patients and 125 healthy control (HC) subjects were collected at Seoul National University Hospital and Inje University Haeundae Hospital. In addition, tissue samples were evaluated from 5 BT patients and 5 HC subjects collected at Seoul National University Hospital. Each BT clinical subject exhibited symptoms leading to a hospital visit for treatment. Health care subjects were selected through general health checkups. Experimental examples of the present invention were approved by the Seoul National University Hospital Clinical Trial Review Board (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 for 15 minutes at 4°C. All brain tissue samples were frozen in liquid nitrogen and stored at -80°C for analysis.

Experimental Example 2. In vivo mouse study 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 on a 12-hour day and night cycle throughout the course of the in vivo study.

Experimental Example 3. EV DNA extraction and sequencing

To extract EV from serum and tissue samples, blood was added to a 10 ml tube and centrifuged (3,500 x g, 10 min, 4° C.) to settle the suspension and collect only the supernatant, and then transferred to a new 10 ml tube. After removing bacteria and foreign substances from the collected supernatant using a 0.22 μm filter, transfer to a centripreigugal filter 50 kD, centrifuge at 1500 xg, 4° C. for 15 minutes, discarding substances smaller than 50 kD and discarding 10 ml Concentrated to. Once again, after removing bacteria and foreign substances using a 0.22 ㎛ filter, discard the supernatant using a ultra-high-speed centrifugation method at 150,000 xg at 4℃ 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 were boiled at 100° C. to allow the DNA inside to come out of the 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 for 30 minutes at 4°C, and only the supernatant was collected, and then the amount of DNA was quantified using Nanodrop. Thereafter, in order to confirm whether the bacterial-derived DNA was present in the extracted DNA, PCR was performed with the 16s rDNA primer shown in Table 1 below, and it was confirmed that the bacterial-derived gene was present in the extracted gene.

primer order Sequence number 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). The classification (Taxonomic assignment) was performed with the profiling program MDx-Pro ver.2 (MD Healthcare, Korea). The paired-end reads were filtered according to the barcode, and the 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 Phred quality scores less than 20 were excluded. Based on the 97% similarity threshold using the VSEARCH de novo clustering method, OTU (Operational Taxonomic Unit) was classified into genus levels. OTUs containing 1 sequence in only one sample were excluded from further analysis. Subsequently, classification level designation was performed at the species level using UCLUST and QIIME 1.9.1 for the Silva 132 database under basic parameters. When the taxonomic information in the database was insufficient and the cluster could not be allocated at the genus level, the taxon was designated as the next highest level. Parentheses around the taxonomy denote a proposed taxonomic assignment, which has not been identified based primarily on the whole genome phylogeny within the genome database.

Experimental Example 5. Establishment of predictive diagnostic model

To develop a predictive diagnostic model for brain tumor (BT), the relative abundance of OTU at the genus level was considered as a model variable.

First, a candidate strain having a p value of less than 0.01, a fold change greater than 2 times, and an average relative abundance greater than 0.1% was selected. The strains selected as model variables were 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 AIC (Akaike information criterion) is used to compare predictive diagnostic models with different variables. The second method (M2) combined age and sex 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. The fourth method (M4) included age and sex as covariates in addition to incorporating selected strain markers using LEfSe. In addition, the fifth method (M5) was calculated by a machine learning algorithm based on the gradient boosting machine (GBM) ensemble method.

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

Experimental Example 6. Statistical Analysis

Significant age differences between BT and control were determined through Student's t-test and Wilcoxon ranksum test, respectively. A Chi-square test was performed to determine the statistical difference between groups based on the sex of the cohort. Praycipal coordinate analysis (PCoA) was performed to determine the individual classification level clusters of the groups according to the 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 abundance 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 the p value was 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 the 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 were between the ages of 40 and 78, the average age was 59.7 (SD 10.5), and the BT subjects were between the ages of 16 and 81, and the average age was 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 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) between 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. Confirmation 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 (Figs. 2A and 2C). LEfSe analysis of phylum level biomarkers derived Actinobacteria, Proteobacteria, and Firmicutes as the only phylum with a log (LDA score) value greater than 4 (Fig. 3a).

At the class level, Clostridia, Bacilli, Erysipelotrichia, Gammaproteobacteria, Actinobacteria, Alphaproteobacteria and Negativicutes were significantly changed. Eight river level biomarkers were determined using Actinobacteria, which yielded a log of 4.0 (LDA score), which showed a biologically significant abundance in the control group (FIG. 4A ).

At the order level, Clostridiales, Bifidobacteriales, Erysipelotrichales, Lactobacillales, Micrococcales, Sphingomonadales and Selenomonadales varied significantly 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 the log (LDA score) value of Ruminococcaceae was greater than 4.0 (FIG. 4C).

Finally, as a result of analysis at the genus level, a taxa showing a number of significant differences between the control group and the BT group was determined (FIG. 2B ). Ruminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Lactobacillus, Ruminiclostridium 6 and Peptoclostridium were significantly higher in BT group compared to control, whereas [Eubacterium] coprostanoligenes, Escherichia-Shigella, Blautia, and Bifidobacterium It was found at a significantly lower level (Fig. 2D).

LEfSe analysis of the genus level serum EV microbiome composition yielded a total of 30 genera, especially Ruminococcaceae UCG-014 was found to exceed 4.0 (Fig. 3b). A total of 4, 9, 12, 18, and 29 taxa were higher than 0.5% in all groups, and there were significant differences between the control group and the patient group using t-test in the mun, river, neck, and department (p <0.05).

Example 4. Development of a diagnostic model for brain tumor (BT) based on serum microbial EV metagenome

Using the serum microbial EV metagenomic profile 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 29 different important microbial genus EVs, 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, Ruminococcaceae(f), Ruminococcus 2, Peptococcaceae(f), Ruminococcus 2, Peptococcaceae(f), Diaphorobacter, 1, Diaphorobacter, Solanum melongena, Actinomyces, Knoellia, Stenotrophomonas, and Veillonella were calculated.

In the case of M5, the relative abundance of the analyzed total microbial EV metagenomic information was input 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 an 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 a brain tumor (BT) diagnostic model based on brain tissue microbial EV metagenome

At the phylogenetic level, Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria were the most abundant phyla in all groups, accounting for more than 80% of the 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 remarkably abundant in the control, while Erysipelotrichales was 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 found through LEfSe were Bacteroidales S24-7 group. , Ruminococcaceae, Prevotellaceae, Bacteroidaceae and Erysipelotrichaceae (Fig. 7c).

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

In addition, in order to compare the changes in the microbiome composition of serum and tissues between the control group and the patient, the fold change of the microbial EV composition of serum and tissues obtained from the same individual was analyzed.

At the phylum level, the patient's Saccaribacteria were significantly reduced in both serum and tissues (Fig. 8A).

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

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

At the family level, Erysipelotrichiales was significantly higher in the patient's serum and tissues, and Prevotellaceae was 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, Lachnospiraceae NK4A136 was significantly lower in the tissues of brain tumor patients, but it was confirmed that it was significantly increased in the serum of patients compared to the tissues and serum samples of the control group (FIG. 9).

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

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

To separate the vesicles present in the blood and extract DNA, first put the blood in a 10 ml tube and centrifuge (3,500 xg, 10min, 4℃) to settle the suspension and collect only the supernatant. Moved. After removing bacteria and foreign substances from the collected supernatant using a 0.22 μm filter, transfer to a centripreigugal filter 50 kD, centrifuge at 1500 xg, 4° C. for 15 minutes, discarding substances smaller than 50 kD and discarding 10 ml Concentrated to. Once again, after removing bacteria and foreign substances using a 0.22 ㎛ filter, discard the supernatant using a ultra-high-speed centrifugation method at 150,000 xg at 4℃ 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 were boiled at 100° C. to allow the DNA inside to come out of the 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 for 30 minutes at 4°C, and only the supernatant was collected, and then the amount of DNA was quantified using Nanodrop. Subsequently, in order to check whether the bacterial-derived DNA was present in the extracted DNA, PCR was performed with the 16s rDNA primer shown in Table 1, 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 a nucleotide quality score file using GS FLX software (v2.9), and then check the credit rating of the lead, and 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 the read length of 300 bps or more was used (Sickle version 1.33). For the result analysis, the 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 genus, 90% for family, 85% for order, 80% for class, and 75% for phylum. Classification of the phylum, river, order, family, and genus levels of OTU was performed, and bacteria having a sequence similarity of more than 97% were analyzed using BLASTN and GreenGenes' 16S DNA sequence database (108,453 sequences) (QIIME).

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

In the method of Example 6-1, after separating vesicles from the blood of 170 brain tumor patients and 200 normal individuals whose age and sex were matched, metagenome sequencing was performed. To develop a diagnostic model, first, a strain with a p value of 0.05 or less between the two groups and a group average of 0.1% or more was selected in the t-test, and then AUC (area under curve), which is a diagnostic performance index, using a logistic regression analysis method. Sensitivity, and specificity were calculated.

As a result of analyzing bacterial-derived vesicles in the blood at the phylum level, when a diagnostic model was developed with Actinobacteria and Tenericutes bacterium biomarkers, the diagnostic performance for brain tumors was significantly shown (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 bacterial-derived vesicles in the blood at the class level, when a diagnostic model was developed with Actinobacteria and Mollicutes strong bacterial biomarkers, the diagnostic performance for brain tumors was significantly shown (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 Turicibacterales, Xanthomonadales, and RF39 neck 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, the diagnostic performance for brain tumors when a diagnostic model was developed with bacterial biomarkers of Intrasporangiaceae, Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nocardiaceae, Xanthomonadaceae, Weeksellaceae, and Veillonellaceae families. Was significantly shown (see Table 6 and Fig. 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__[Weeksellaceae] 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 the blood at the genus level, when a diagnostic model was developed with bacterial biomarkers of the genus Stenotrophomonas, Lactobacillus, Turicibacter, Tepidimonas, Sphingomonas, Rhodococcus, and Cloacibacterium, the diagnostic performance for brain tumors was significant. (See Table 7 and Fig. 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 summarizes the results of bacterial-derived vesicle analysis in serum, blood, and brain tissue samples.

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

The above-described description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains can understand that it is possible to easily transform it 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 limiting.

<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)

Information providing method for diagnosis of brain tumor, 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 a primer pair of SEQ ID NO: 1 and SEQ ID NO: 2; And
(c) comparing the increase or decrease in the content of the sample derived from a normal person and extracellular vesicles derived from bacteria through the sequence analysis of the PCR product.
The method of claim 1,
The normal person and the subject sample is blood or tissue,
In the step (c), Actinobacteria, Proteobacteria, Firmicutes, Bacteroidetes, Cyanobacteria, Saccharibacteria, and Tenericutes Extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of,
Clostridia, Basil, Erysipelotrichiam, Gammaproteobacteria, Alphaproteobacteria, Negativicutes, Saccharii Extracellular vesicles derived from one or more class bacteria selected from the group consisting of Saccharibacteria (p), Chloroplast, Actinobacteria, and Molycutes,
Clostridiales, Bifidobacteriales, Erysipelotrichales, Lactobacillales, Micrococcales, Sphingomonadales, Selenomonas (Selenomonadales), Chloroplast (c)), Saccharibacteria (p)), Turicibacterales, and Xanthomonadales, one selected from the group consisting of Extracellular vesicles derived from the above order bacteria,
Luminococcaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, Streptococcaceae, Streptococcaceae, Bifidobacteriaceae, Bifidobacteriaceae, Bifidobacteriaceae Spingomonadaceae, Prophyromonadaceae, Bacteroidaceae, Bacteroidales S24-7, Chloroplast (c)), Prevotellaceae ), Saccharibacteria (p), Intrasporangiaceae, Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nokar Extracellular vesicles derived from one or more family bacteria selected from the group consisting of Nocardiaceae, Xanthomonadaceae, Weeksellaceae, and Veillonellaceae, or
Luminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Ruminiclostridium 6, Peptoclostridium, Eubacterium Coprostanoligenes ([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 A method for providing information for diagnosis of brain tumor, characterized in that comparing the increase or decrease in the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of.
The method of claim 2,
The normal person and the subject sample are blood,
In step (c), compared with a sample derived from a normal person,
Tenericutes (Tenericutes) phylum bacteria-derived extracellular vesicles,
Extracellular vesicles derived from Mollicutes class bacteria,
Extracellular vesicles derived from the order bacteria of Turicibacterales,
At least one selected from the group consisting of Lactobacillaceae, Mogibacteriaceae, Turicibacteraceae, Nocardiaceae, and Weeksellaceae. 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 A method for providing information for diagnosis of a brain tumor, characterized in that when the content of the outer vesicle is increased, it is diagnosed as a brain tumor.
The method of claim 2,
The normal person and the subject sample are blood,
In step (c), compared with a sample derived from a normal person,
Extracellular vesicles derived from Actinobacteria phylum bacteria,
Extracellular vesicles derived from Actinobacteria class bacteria,
Xanthomonadales (Xanthomonadales) order bacteria-derived extracellular vesicles,
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, the brain tumor is diagnosed as a brain tumor How to provide information for diagnosis.
The method of claim 2,
The blood is whole blood, serum, plasma, or blood mononuclear cells, characterized in that, information providing method for brain tumor diagnosis.
The method of claim 5,
The normal and subject samples are serum,
In step (c), compared with a sample derived from a normal person,
Extracellular vesicles derived from Firmicutes,
Extracellular vesicles derived from one or more class bacteria selected from the group consisting of Clostridia, Bacil, and Erysipelotrichiam,
Extracellular vesicles derived from one or more order bacteria selected from the group consisting of Clostridiales, Bifidobacteriales, and Erysipelotrichales,
Extracellular derived from one or more family bacteria selected from the group consisting of Luminococcaceae, Lactobacillaceae, Peptostreptococcaceae, and Erysipelotrichaceae. Parcel, or
Luminococcaceae UCG-014, Lachnospiraceae NK4A136, Ruminococcaceae UCG-013, Lactobacillus, Ruminiclostridium 6, Peptoclostridium When the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of (Peptoclostridium) is increased, it is diagnosed as a brain tumor, a method for providing information for brain tumor diagnosis.
The method of claim 5,
The normal and subject samples are serum,
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 one or more class bacteria selected from the group consisting of Gammaproteobacteria, Actinobacteria, Alphaproteobacteria, and Negativicutes,
Extracellular vesicles derived from one or more order bacteria selected from the group consisting of Lactobacillales, Micrococcales, Sphingomonadales, and Selenomonadales,
One or more families selected from the group consisting of Lachnospiraceae, Streptococcaceae, Bifidobacteriaceae, Sphingomonadaceae, and Porphyromonadaceae ( family) extracellular vesicles derived from bacteria, or
Eubacterium coprostanoligenes, Escherichia-Shigella, Blautia, Bifidobacterium, Streptococcus, and Sphingomonas Sphingomonas), characterized in that when the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of a reduced content is diagnosed as a brain tumor, information providing method for brain tumor diagnosis.
The method of claim 2,
The normal person and the subject sample are tissues,
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,
Extracellular vesicles derived from bacteria of the order Erysipelotrichales,
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, it is characterized as a brain tumor How to provide information for brain tumor diagnosis.
The method of claim 2,
The normal person and the subject sample are tissues,
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 Cyanobacteria, and Saccharibacteria,
Extracellular vesicles derived from one or more class bacteria selected from the group consisting of Clostridia, Saccharibacteria (p), and Chloroplast,
Extracellular vesicles derived from one or more order bacteria selected from the group consisting of the order Clostridiales, the order Chloroplast (c), and the order Saccharibacteria (p),
Luminococcaceae, 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
Bacteroidales S24-7, Chloroplast (c), Lachnospiraceae NK4A136, Prevotella 9, and Candidatus Saccharimonas When the content of extracellular vesicles derived from one or more genus bacteria selected from the group is reduced, the diagnosis is made as a brain tumor.
The method of claim 2,
The tissue is a brain tissue, characterized in that, information providing method for brain tumor diagnosis.

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