WO2021049834A1 - Method for diagnosing colorectal cancer on basis of metagenome and metabolite of extracellular vesicles - Google Patents

Abstract

The present invention relates to a method for diagnosing colorectal cancer on the basis of a metagenome and metabolites of extracellular vesicles. The method for diagnosing colorectal cancer according to the present invention may diagnose and predict colorectal cancer early through metagenomic analysis of extracellular vesicles secreted by intestinal bacteria and analysis of metabolites in the extracellular vesicles may confirm the presence of extracellular vesicles derived from bacteria with significantly changed content from colorectal cancer and metabolites such as amino acids, amino alcohols, aromatic alcohols, carboxylic acids, and fatty acids, and may confirm association of the extracellular vesicles and the metabolites to use together as biomarkers, thereby improving the accuracy of colorectal cancer diagnosis, lower the incidence of colorectal cancer, and increase therapeutic effects.

Description

Metagenomic and metabolite-based colon cancer diagnosis method of extracellular vesicles

The present invention relates to a method for diagnosing colon cancer based on metagenomic and metabolite analysis of bacterial-derived extracellular vesicles.

This application claims priority based on Korean Patent Application No. 10-2019-0111921 filed on September 10, 2019 and Korean Patent Application No. 10-2020-0019537 filed on February 18, 2020, and All contents disclosed in the specification and drawings of the application are incorporated in this application.

Colorectal cancer (CRC) is one of the most common cancers and is the second leading cause of cancer-related deaths worldwide. Most cases of colorectal cancer occur sporadically, and environmental factors appear as the major cause of the disease. Diet has long been considered the most important lifestyle risk factor for colorectal cancer. In vivo and in vitro studies have investigated the effect of protein intake on the risk of colorectal cancer, and it has been reported that a high protein diet can cause DNA damage in colorectal cancer, reduce the thickness of the colonic mucosa, and decrease the height of microvilli in colon cells. There is a bar. There is a growing concern that the incidence of colorectal cancer increases due to changes in human intestinal microflora induced by this diet. Therefore, researchers believe that tumors occur preferentially in the colon and rectum where approximately 70% of the host microbiota settled. , Intestinal microbiota is important for the initiation and progression of colon cancer. Microorganisms have the potential to create a microenvironment that aids colon cancer by secreting mediators such as interleukin, tumor necrosis factor-alpha (TNF-alpha), and reactive oxygen species, and metabolites of intestinal microbes increase the risk of colon cancer. Can be increased. For example, when the content of acetaldehyde produced by intestinal microbes is high, folic acid in the intestine is decomposed, thereby increasing the risk of colon cancer.

In the case of early colorectal cancer, no specific symptoms appear, but even in the absence of symptoms, blood loss due to inconspicuous intestinal bleeding can lead to anemia, sometimes leading to loss of appetite and weight loss. In the case of advanced cancer, changes in bowel habits, such as pain in the stomach, diarrhea or constipation, may appear, symptoms of rectal bleeding from the anus may appear, and the blood may appear bright red or black. If colon cancer has progressed, a lump that was not normally touched on the stomach may be touched. Symptoms that should be noted most are changes in bowel habits, bloody stool, pain, and anemia. Especially, when such changes occur in adults over 40 years of age, it is necessary to thoroughly investigate.

Colon cancer can be confirmed only when cancer cells are detected through biopsy through colonoscopy. Since most of the colon cancer has no symptoms at an early stage, it is quite difficult to diagnose, and there is currently no method of predicting colon cancer by a non-invasive method. As the existing diagnostic method, it is often detected when solid cancer such as colon cancer has progressed, so in order to prevent the medical cost and death of colon cancer, the occurrence and causative factors of colon cancer are predicted in advance, and the occurrence of colon cancer in the high-risk group. It is an efficient way to provide a way to prevent it.

On the other hand, it is known that the number of microorganisms living symbiotically in the human body reaches 100 trillion, 10 times more than human cells, and the number of genes in microorganisms is more than 100 times the number of human genes. Microbiota (microbiome) refers to a microbial community including bacteria, archaea, and eukaryotes (eukarya) present in a given habitat, and the intestinal microbiota plays an important role in human physiological phenomena. It is known to have a great influence on human health and disease through interaction with human cells. Recently, it has been reported that colon cancer is caused by intestinal bacteria containing specific toxins such as metaloprotease.

Microbial-derived extracellular vesicles are emerging as an important new research topic in understanding the intersection of gut microbiota and human health. It is well known that intestinal microbes secrete several types of extracellular vesicles (EVs), including outer membrane vesicles, shedding vesicles, and apoptotic bodies. Extracellular vesicles are mainly composed of lipids, proteins, nucleic acids and metabolites, and the underlying mechanisms are still unclear, but their main role is to move active biomolecules into cells over long distances to provide drug delivery to target sites or to regulate host cell responses. It is to do.

Recent studies have provided mechanistic evidence for the involvement of intestinal microbes in the progression of colon cancer. In vivo studies have shown that mutant mice, which are genetically affected by colon cancer incidence, have significantly fewer tumors under aseptic conditions than mice with conventional microorganisms. In addition, Enterococcus faecalis and Eschelichia coli generate free radicals that target DNA, which can contribute to extracellular genotoxicity and colon cancer. However, it is not yet clear what diseases the bacteria produce in the gut.

Accordingly, the present inventors developed a method for diagnosing colon cancer through metagenomic analysis and metabolite analysis by separating bacterial-derived extracellular vesicles from colon cancer patients and normal individuals using 16S rDNA amplicon sequencing and metabolites.

In order to diagnose colorectal cancer, the present inventors isolate bacterial-derived extracellular vesicles from feces samples of normal persons and subjects, extract genes from them, perform metagenomic analysis, and analyze metabolites in the extracellular vesicles. Bacterial-derived extracellular vesicles and metabolites that can act as causative factors of colon cancer were identified, and the present invention was completed based on this.

Accordingly, the present invention comprises the steps of: (a) separating a sample from a normal person and a subject;

(b) separating extracellular vesicles from the sample;

(c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

(d) Comprising the step of comparing the increase or decrease of metabolite content through metabolite analysis in the isolated extracellular vesicles, a method for providing information for diagnosis of colon cancer, a method for providing information for predicting the onset of colon cancer, and a large intestine It aims to provide a method for diagnosing cancer.

However, the technical task to be achieved by the present invention is not limited to the above-mentioned tasks, and other tasks that are not mentioned can be clearly understood by those of ordinary skill in the technical field to which the present invention belongs from the following description. There will be.

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

(a) separating the normal person and the subject sample;

(b) separating extracellular vesicles from the sample;

(c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

(d) comparing the increase or decrease of the content of metabolites through metabolite analysis in the isolated extracellular vesicles.

In addition, the present invention provides an information providing method for predicting the onset of colon cancer, comprising the following steps:

(a) separating the normal person and the subject sample;

(b) separating extracellular vesicles from the sample;

(c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

(d) comparing the increase or decrease of the content of metabolites through metabolite analysis in the isolated extracellular vesicles.

In addition, the present invention provides a method for diagnosing colon cancer, comprising the following steps:

(a) separating the normal person and the subject sample;

(b) separating extracellular vesicles from the sample;

(c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

(d) comparing the increase or decrease of the content of metabolites through metabolite analysis in the isolated extracellular vesicles.

As an embodiment of the present invention, the sample may be feces, but is not limited thereto.

In another embodiment of the present invention, the metabolite is selected from the group consisting of amino acids, amino alcohols, aromatic alcohols, carboxylic acids, and fatty acids. It may be one or more, but is not limited thereto.

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

My solution Tino access (Actinomyces), Bifidobacterium (Bifidobacterium), Loti Ah (Rothia), propionic sludge tumefaciens (Propionibacterium), Colin Cellar (Collinsella), Les night interrogating two months (Bacteroidales) S24-7 group (f) , chloro plast (Chloroplast) (o), Blau thiazole (Blautia), La pants Clostridium (Lachnoclostridium), La pants at Spira (Lachnospiraceae) NK4A136 when the group La pants Spira (Lachnospiraceae) UCG-008, Dorea , Eubacterium coprostanoligenes group, Ruminococcaceae UCG-002 , Ruminococcus 2 , Subdoligranulum , Rumi Ruminococcaceae (f), Ruminococcaceae UCG-014 , Faecalibacterium , Ruminococcaceae NK4A214 group, Staphylococcus , Ka I'll tumefaciens (Catenibacterium), Parr ratio Pseudomonas (Parvimonas), Rumi Nicklaus tree Stadium (Ruminiclostridium) 5, methyl tumefaciens (Methylobacterium), Solar titanium Mello dehydrogenase (Solanum Melongena), Sphingomonas (Sphingomonas), dia captive bakteo (Diaphorobacter), Escherichia - Shigella (Escherichia-shigella), Proteus (Proteus), Pseudomonas (Pseudomonas), Enterobacter (Enterobacter), four Kari bacteria (Saccharibacteria) (p), and the molar Riku Tess (Mollicutes) RF9 to (o) 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, but is not limited thereto.

As another embodiment of the present invention, compared to the normal person in the step (c), Firmicutes phylum bacteria-derived extracellular vesicles, or

Bifidobacterium (Bifidobacterium), Colin Cellar (Collinsella), Blau Tia (Blautia), La Chino Clostridium (Lachnoclostridium), referred to at Chino Spira (Lachnospiraceae) UCG-008, Toray Ah (Dorea), Aust tumefaciens Eubacterium coprostanoligenes group, Ruminococcaceae UCG-002 , Ruminococcus 2 , Subdoligranulum , Ruminococcaceae ( f), Faecalibacterium , Ruminococcaceae NK4A214 group, Catenibacterium , Parvimonas , Ruminiclostridium 5 , Diaphorobacter ( Diaphorobacter ), and Enterobacter ( Enterobacter ) When the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of is increased, it can be predicted that the risk of developing colon cancer will be high, but is not limited thereto.

As another embodiment of the present invention, in the step (c) compared to a normal person, proteobacteria ( Proteobacteria ) phylum bacteria-derived extracellular vesicles, or

Actinomyces , Rothia , Propionibacterium , Bacteroidales S24-7 group (f), Chloroplast (o), Lacinospira Lachnospiraceae NK4A136 group, Ruminococcaceae UCG-014 , Staphylococcus , Methylobacterium , Solanum melongena , Sphingomonas , Escherichia - Shigella (Escherichia-shigella), Proteus (Proteus), Pseudomonas (Pseudomonas), four Kariya bacteria (Saccharibacteria) (p), and the mole Riku test (Mollicutes) is selected from the group consisting of RF9 (o) If the content of the extracellular vesicles derived from one or more genus bacteria is reduced, it can be predicted that the risk of developing colon cancer is high, but is not limited thereto.

As another embodiment of the present invention, in the step (d), leucine, isoleucine, alanine, lysine, thyramine, aminoisobutyric acid, ethanolamine (Ethanolamine), Phenol, Furoic acid, Succinic acid, Oxalic acid, Butanoic acid, Hexanoic acid, Palmitic acid, And oleic acid (Oleic acid) can be compared to increase or decrease the content of one or more metabolites selected from the group consisting of, but is not limited thereto.

In another embodiment of the present invention, in step (d), compared to a normal person, leucine, isoleucine, alanine, lysine, thyramine, ethanolamine, phenol (Phenol), furoic acid, succinic acid, oxalic acid, hexalic acid, palmitic acid, palmitic acid, and oleic acid If the content of one or more metabolites is increased, it can be predicted that the risk of developing colon cancer is high, but the present invention is not limited thereto.

As another embodiment of the present invention, in the step (d), when the content of aminoisobutyric acid or butanoic acid is reduced compared to a normal person, it can be predicted that the risk of developing colorectal cancer is high. , Is not limited thereto.

As another embodiment of the present invention, the content of extracellular vesicles derived from bacteria of Collinsella genus is increased compared to normal people in step (c) and Solanum melongena genus The content of bacterial-derived extracellular vesicles is reduced,

In the step (d), when leucine and oxalic acid are increased compared to normal people, it can be predicted that the risk of developing colorectal cancer is high, but the present invention is not limited thereto.

The method for diagnosing colorectal cancer according to the present invention can diagnose and predict colon cancer early through metagenomic analysis of extracellular vesicles secreted from intestinal bacteria and metabolite analysis in extracellular vesicles. Changed bacteria-derived extracellular vesicles and metabolites such as amino acids, amino alcohols, aromatic alcohols, carboxylic acids, fatty acids, etc. are identified, and their associations are checked and used together as a biomarker to improve the accuracy of colon cancer diagnosis. It is expected to lower the incidence rate and increase the therapeutic effect.

1A is a view showing a result of comparing alpha diversity using Chao1 index and Shannon index in extracellular vesicles derived from colon cancer patients and normal persons according to an embodiment of the present invention.

1B is a phylum (left) and genus (right) level through principal coordinate analysis (pCoA) in the extracellular vesicles derived from colon cancer patients and normal persons according to an embodiment of the present invention. It is a diagram showing the result of comparing the beta diversity.

2A and 2B show the distribution of bacterial-derived extracellular vesicles significantly changed at the phylum level through metagenomic analysis of extracellular vesicles derived from colon cancer patients and normal persons according to an embodiment of the present invention, FIG. 2A is a diagram showing a heat map, and FIG. 2B is a diagram showing a bar graph.

2C and 2D show the distribution of bacterial-derived extracellular vesicles significantly changed at the genus level through metagenomic analysis of extracellular vesicles derived from colon cancer patients and normal persons according to an embodiment of the present invention. FIG. 2C is a diagram depicted as a heat map, and FIG. 2D is a diagram depicted as a bar graph.

3A is a view showing the result of taxonomic analysis at the phylum level of bacterial-derived extracellular vesicles isolated from colon cancer patients and normal people according to an embodiment of the present invention.

3B is a view showing the results of taxonomic analysis at the genus level of bacterial-derived extracellular vesicles isolated from colon cancer patients and normal people according to an embodiment of the present invention.

Figures 4a and 4b confirm the results of analyzing metabolites in colorectal cancer patients and normal people according to an embodiment of the present invention using gas chromatography time-of-flight mass spectrometry, and Figure 4a is a three-dimensional PCA It is a figure showing a score plot, and FIG. 4B is a figure showing a loading plot of PC1 and PC2.

5A is a view confirming the association between bacterial-derived extracellular vesicles and metabolites according to an embodiment of the present invention through Pearson correlation analysis.

5B is a diagram showing a receiver operation characteristic (ROC) curve by performing binary logistic regression analysis on bacterial-derived extracellular vesicles and metabolite biomarkers according to an embodiment of the present invention. .

The present invention provides a method for providing information for diagnosing colorectal cancer, comprising the following steps:

(a) separating the normal person and the subject sample;

(b) separating extracellular vesicles from the sample;

(c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

(d) comparing the increase or decrease of the content of metabolites through metabolite analysis in the isolated extracellular vesicles.

In addition, the present invention provides an information providing method for predicting the onset of colon cancer, comprising the following steps:

(a) separating the normal person and the subject sample;

(b) separating extracellular vesicles from the sample;

(c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

(d) comparing the increase or decrease of the content of metabolites through metabolite analysis in the isolated extracellular vesicles.

In the present invention, the normal and subject samples may be feces, but are not limited thereto.

In the present invention, in the step (c), the extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of Firmicutes and Proteobacteria, or

My solution Tino access (Actinomyces), Bifidobacterium (Bifidobacterium), Loti Ah (Rothia), propionic sludge tumefaciens (Propionibacterium), Colin Cellar (Collinsella), Les night interrogating two months (Bacteroidales) S24-7 group (f) , chloro plast (Chloroplast) (o), Blau thiazole (Blautia), La pants Clostridium (Lachnoclostridium), La pants at Spira (Lachnospiraceae) NK4A136 when the group La pants Spira (Lachnospiraceae) UCG-008, Dorea , Eubacterium coprostanoligenes group, Ruminococcaceae UCG-002 , Ruminococcus 2 , Subdoligranulum , Rumi Ruminococcaceae (f), Ruminococcaceae UCG-014 , Faecalibacterium , Ruminococcaceae NK4A214 group, Staphylococcus , Ka I'll tumefaciens (Catenibacterium), Parr ratio Pseudomonas (Parvimonas), Rumi Nicklaus tree Stadium (Ruminiclostridium) 5, methyl tumefaciens (Methylobacterium), Solar titanium Mello dehydrogenase (Solanum Melongena), Sphingomonas (Sphingomonas), dia captive bakteo (Diaphorobacter), Escherichia - Shigella (Escherichia-shigella), Proteus (Proteus), Pseudomonas (Pseudomonas), Enterobacter (Enterobacter), four Kari bacteria (Saccharibacteria) (p), and the molar Riku Tess (Mollicutes) RF9 to (o) Colorectal cancer may be diagnosed 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, but is not limited thereto.

In the present invention, (p), (o), or (f) described after the name of a specific bacteria among the bacteria is a phylum, an order, or a family, and the analyzed sequence is a phylum ( (p), (o), and (f) are indicated above only for bacteria that matched only up to the phylum, order, or family level, all of which belong to the bacteria identified at the genus level. . In other words, the bacteria whose names (p), (o), or (f) are listed above have a genus level, but the sequence to identify them is not accurate or there is no DB itself. It refers to the bacteria that have been identified in, but only matched up to the level of the phylum, order, or family.

In the present invention, in the step (d), leucine, isoleucine, alanine, lysine, tyramine, aminoisobutyric acid, ethanolamine, Phenol, Furoic acid, Succinic acid, Oxalic acid, Butanoic acid, Hexanoic acid, Palmitic acid, and Oleic acid acid) can be diagnosed by comparing the increase or decrease in the content of one or more metabolites selected from the group consisting of, but is not limited thereto.

In the present invention, the method may be performed in comparison with a normal control (normal person) sample, and the normal control is a test target of a potential patient (i.e., the same individual as the patient subject to the test in step (a)). This means that it includes all samples taken from a normal person in the sample or from other normal individuals (individuals without colorectal cancer). At this time, in the sample collected from the potential patient (subject), compared to the normal person, Firmicutes phylum bacteria-derived extracellular vesicles, or

Bifidobacterium (Bifidobacterium), Colin Cellar (Collinsella), Blau Tia (Blautia), La Chino Clostridium (Lachnoclostridium), referred to at Chino Spira (Lachnospiraceae) UCG-008, Toray Ah (Dorea), Aust tumefaciens Eubacterium coprostanoligenes group, Ruminococcaceae UCG-002 , Ruminococcus 2 , Subdoligranulum , Ruminococcaceae ( f), Faecalibacterium , Ruminococcaceae NK4A214 group, Catenibacterium , Parvimonas , Ruminiclostridium 5 , Diaphorobacter ( Diaphorobacter ), and Enterobacter ( Enterobacter ) When the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of an increased amount may be determined as colon cancer, but is not limited thereto.

In addition, in a sample taken from the potential patient (subject), compared to a normal person, proteobacteria ( Proteobacteria ) phylum bacteria-derived extracellular vesicles, or

Actinomyces , Rothia , Propionibacterium , Bacteroidales S24-7 group (f), Chloroplast (o), Lacinospira Lachnospiraceae NK4A136 group, Ruminococcaceae UCG-014 , Staphylococcus , Methylobacterium , Solanum melongena , Sphingomonas , Escherichia - Shigella (Escherichia-shigella), Proteus (Proteus), Pseudomonas (Pseudomonas), four Kariya bacteria (Saccharibacteria) (p), and the mole Riku test (Mollicutes) is selected from the group consisting of RF9 (o) If the content of the extracellular vesicles derived from one or more genus bacteria is reduced, it may be determined as colon cancer, but is not limited thereto.

In addition, in the samples collected from the potential patients (subjects), compared to normal subjects, Leucine, Isoleucine, Alanine, Lysine, Tyramine, Ethanolamine, and Phenol ), furoic acid, succinic acid, oxalic acid, hexalic acid, palmitic acid, and at least one selected from the group consisting of oleic acid If the content of the metabolite is increased, it may be determined as colon cancer, but is not limited thereto.

In addition, if the content of aminoisobutyric acid or butanoic acid in the sample collected from the potential patient (subject) is reduced compared to the normal person, it may be determined as colon cancer, but is not limited thereto. .

In the present invention, when diagnosing colorectal cancer, the extracellular vesicles derived from bacteria of Collinsella genus and Solanum melongena genus, and leucine and oxalic acid are used. When using as a biomarker, two ohmic biomarkers in which the bacteria-derived extracellular vesicles and metabolites are combined, the accuracy (AUC) for colon cancer diagnosis can be improved.

Specifically, the (c) is a choline Cellar (Collinsella) genus (genus) bacterial content of the cells derived from outside the package compared to the normal increase in steps, and solar titanium Mello dehydrogenase (Solanum melongena) genus (genus) Bacterial derived extracellular vesicles The content is reduced,

In the step (d), when leucine and oxalic acid are increased compared to normal people, it can be predicted that the risk of developing colorectal cancer is high, but the present invention is not limited thereto.

Therefore, the method of the present invention can be understood as a method of improving accuracy (AUC), and extracellular vesicles derived from bacteria of the genus Collinsella and Solanum melongena genus as markers When used, or when using leucine and oxalic acid as markers, the accuracy may be 90% to 100%, preferably 90% to 98%. The specific value of the level is a threshold value of two numbers selected from the group consisting of 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% and 100%. Includes all range values. In one embodiment of the present invention, a boundary value of 90% and 98% may be specifically selected among the numerical ranges, and accordingly, all values in the range of 90% to 98% are intended in the present invention. It is obvious to a person skilled in the art. In another embodiment (ebodiment) preferably 95% accuracy when using extracellular vesicles derived from bacteria of the genus Collinsella and genus Solanum melongena as markers And, when using leucine and oxalic acid as markers, an accuracy of 92% may be indicated.

In addition, when using the extracellular vesicles derived from bacteria, and leucine (Leucine) and oxalic acid (Oxalic acid) as markers in the present invention Collinsella genus and Solanum melongena genus , The accuracy may be 90% to 100%, preferably the accuracy may represent a level of 95% to 100%. The specific value of the level includes all values of the range using two numbers selected from the group consisting of 95%, 96%, 97%, 98%, 99% and 100% as boundary values. In one embodiment of the present invention, a boundary value of 95% and 100% may be specifically selected among the numerical ranges, and accordingly, all values in the range of 95% to 100% are intended in the present invention. It is obvious to a person skilled in the art. In another embodiment (ebodiment) it is possible to achieve an accuracy of preferably 100%.

The term "metabolites" used in the present invention refers to a generic term for intermediates and products of metabolism, and in the present invention, the metabolites are amino acids, amino alcohols, It may be one or more selected from the group consisting of aromatic alcohols, carboxylic acids, and fatty acids, but is not limited thereto.

The term "metabolome" used in the present invention refers to the total number of metabolites present in biological samples such as cells, tissues, and body fluids. In the present invention, the metabolites are bacteria isolated from fecal samples. It refers to the total number of metabolites present in the derived extracellular vesicles.

The term "colorectal cancer" used in the present invention refers to a malignant tumor occurring in the large intestine (cecum, colon, rectum), and the names of appendix cancer, colon cancer, and rectal cancer are included in colon cancer.

As used herein, the term "diagnosis of colorectal cancer" means to determine whether a patient is likely to develop colorectal cancer, whether the possibility of developing colorectal cancer is relatively high, or whether colorectal cancer 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 colorectal cancer for any specific patient. In addition, the method of the present invention can be used clinically to determine treatment by early diagnosis of colorectal cancer 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 metabolisms. In the present invention, metagenomic analysis was preferably performed using bacterial-derived extracellular vesicles isolated from feces.

In an embodiment of the present invention, metagenomic analysis was performed on genes present in bacterial-derived extracellular vesicles in the stool of normal people and colon cancer patients, and by analyzing each at the level of phylum and genus, colon cancer actually occurred. Bacterial-derived vesicles that can act as the cause of were identified.

More specifically, in one experimental example of the present invention, as a result of analyzing the bacterial-derived vesicle metagenome present in the feces at the phylum and genus level, Firmicutes and Proteobacteria phylum (phylum) germ cells derived from outside the package, and the liquid Martino My process (Actinomyces), Bifidobacterium (Bifidobacterium), Loti Ah (Rothia), propynyl sludge tumefaciens (Propionibacterium), choline Cellar (Collinsella), night interrogating two months less (Bacteroidales) S24-7 group (f), chloro plast (Chloroplast) (o), Blau thiazole (Blautia), La pants Clostridium (Lachnoclostridium), La pants Spirra when the (Lachnospiraceae) NK4A136 group, Chino La Lachnospiraceae UCG-008 , Dorea , Eubacterium coprostanoligenes group, Ruminococcaceae UCG-002 , Ruminococcus 2 , Subdoligranulum , Ruminococcaceae (f), Ruminococcaceae UCG-014 , Faecalibacterium , Ruminococcaceae NK4A214 group, Staphylococcus (Staphylococcus), car'll tumefaciens (Catenibacterium), Parr ratio Pseudomonas (Parvimonas), Rumi Nicklaus tree Stadium (Ruminiclostridium) tumefaciens (Methylobacterium), Solar titanium Mello dehydrogenase (Solanum Melongena) to 5, methyl, Sphingomonas , Diaphorobacter , Escherichia Teeth - Shigella (Escherichia-shigella), Proteus (Proteus), Pseudomonas (Pseudomonas), Enterobacter (Enterobacter), four Kari bacteria (Saccharibacteria) (p), and the molar Riku Tess (Mollicutes) RF9 (o) in (genus ) There was a significant difference in bacterial vesicles between colon cancer patients and normal subjects (see Experimental Example 1).

In addition, in an embodiment of the present invention, metabolites were analyzed using gas chromatography time-of-flight mass spectrometry analysis of bacterial-derived extracellular vesicles in the feces of normal people and colon cancer patients.

More specifically, in one experimental example of the present invention, as a result of analyzing metabolites in extracellular vesicles derived from bacteria in the feces of normal people and colon cancer patients, leucine, isoleucine, alanine, and lysine , Tyramine, Aminoisobutyric acid, Ethanolamine, Phenol, Furoic acid, Succinic acid, Oxalic acid, Butanoic acid , Hexanoic acid (Hexanoic acid), palmitic acid (Palmitic acid), and the content of oleic acid (Oleic acid) there was a significant difference between the colorectal cancer patients and normal subjects (see Experimental Example 2).

Small molecule analysis using gas chromatography mass spectrometry (GC-MS) can be used to link cancer development and gut microbiota. In one experimental example of the present invention, a group of amino acids that were upregulated in colorectal cancer patients by GC-MS in particular was confirmed, and the enhanced amino acid level was closely related to the risk of colorectal cancer. Causes include changes in dietary habits, such as high protein intake, which has long been considered the most important lifestyle risk factor for colorectal cancer; Inflammation that reduces the absorption of nutrients in cancer patients; These include fermentation of bacteria in the distal colon of colon cancer patients to break down protein in food, resulting in higher levels of amino acid metabolites in the stool.

In one experimental example of the present invention, as a result of confirming the correlation between the intestinal microbial community and specific metabolites through Pearson rank correlation analysis, the content of most metabolites was positively correlated with Firmicutes phylum bacteria, There was a negative correlation with Proteobacteria phylum bacteria (see Experimental Example 3).

Firmicutes can catalyze amino acids for energy recycling, while Proteobacteria can break down amino acids, including indigestible proteins. Such changes in the intestinal microbial community preferentially affect amino acid metabolism. Short-chain fatty acids, including butanoic acid and aminoisobutyric acid, are well-established sources of energy in the human gut. These microbial metabolites play a role in regulating host metabolism and immune responses. In the present invention, through the above experimental examples, it was proved that diet-related short-chain fatty acids prevent disease and have an effect on treatment for colon cancer.

In one experimental example of the present invention, two metabolites (leucine and oxalic acid) and two genus bacteria ( Colinsella and Solanum melongena )-derived extracellular vesicles were diagnosed with colon cancer through binary logistic regression analysis. When the biomarker was selected as the optimal biomarker for and the combination of the biomarkers was used, it was confirmed that the accuracy of diagnosis was higher than that of using a single ohmic biomarker (see Experimental Example 4).

The present invention analyzes the increase or decrease of the content of bacterial-derived extracellular vesicles in normal people and colon cancer patients by performing metagenomic analysis on bacterial-derived extracellular vesicles isolated from feces through the experimental results as described above, and extracellular vesicles It was confirmed that colorectal cancer can be diagnosed by analyzing the increase or decrease of the metabolite content in. In addition, by confirming the association between bacteria and metabolites in bacterial extracellular vesicles, it was found that changes in intestinal microflora can change the metabolism of metabolites, especially amino acids, and the bacterial-derived extracellular vesicles and metabolite biomarkers It was confirmed that the accuracy of colorectal cancer diagnosis can be improved when two ohmic biomarkers are used in combination.

In the entire specification of the present application, when a certain part “includes” a certain constituent element, it means that other constituent elements may be further included rather than excluding other constituent elements unless otherwise stated. As used throughout the present specification, the term "step to" or "step of" does not mean "step for".

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]

Example 1. Research subject

32 colon cancer patients at Seoul National University Bundang Hospital and Chung-Ang University Hospital and 40 healthy control subjects (hereinafter, normal people) at Haeundae Paik Hospital participated from April 2016 to April 2018. Colorectal cancer patients were first diagnosed in 2013 according to the diagnostic criteria presented by the International Union Against Cancer and the American Joint Committee on Cancer. Patients were examined for patient characteristics such as age, sex, stage, tumor location, and carcinoembryonic antigen (CEA) test, and healthy subjects visited the hospital for regular health check-ups. After examination, a healthy control group was selected that had no known disease and was found to be normal as a result of laboratory tests. Exclusion criteria for healthy controls included intestinal disease diagnosis, intestinal disease treatment, and previous colon cancer diagnosis. For healthy controls, general characteristics including age, sex and medical history were recorded. Exclusion criteria for patients and healthy controls included recurrence of colorectal cancer immediately after surgery, chemotherapy, other cancers or metabolic diseases due to complications of colorectal cancer, or antibiotic treatment within 1 month of sample collection. The experiment of the present invention was conducted with the approval of the Clinical Trial Committee of Seoul National University Bundang Hospital (IRB No. B-1708/412-301) and Haeundae Paik Hospital (IRB No. 129792-2015-064), and consent to the provision of information from all subjects Got it.

Example 2. Sample collection and extracellular vesicle (EV) separation

Fecal samples were taken before surgery or intestinal lavage, and all subjects consumed soft food, quit smoking, and abstaining from drinking one day before the sample was collected. Fecal samples were collected in the center of the feces using a sterile cotton swab and stored at -20°C. Before separating the extracellular vesicles of bacteria from the feces sample, the feces sample (1 g) was mixed with 10 mL of phosphate buffered saline (PBS). After emulsification, it was shaken for 24 hours. Then, the sample was cultured to separate the extracellular vesicles from human feces, and the extracellular vesicles of the stool sample were separated using fractional centrifugation at 10,000 x g for 10 minutes at 4°C. Bacteria and foreign substances contained in the supernatant were completely removed by filtration through a filter having a diameter of 0.22 μm.

Example 3. Mass spectrometry using gas chromatography

Frozen extracellular vesicle (EV) samples were thawed at 4° C., and each 50 μL of sample was diluted with 1 mL of acetonitrile: isopropanol: water (3:3:2) mixture. Then, the mixed sample was stirred for 5 minutes and then centrifuged for 5 minutes at 18,341 xg at 4°C. Thereafter, 400 μL of the supernatant was evaporated, completely dried at room temperature, and reconstituted to 400 μL using 50% acetonitrile. After repeating the centrifugation as described above, the supernatant was evaporated and reconstituted with 10 μL of methoxyamine dissolved in pyridine, and kept at 30° C. while shaking for 90 minutes. Then, after cooling the sample to room temperature, 90 μL of a mixture of MSTFA ( N- Methyl- N- (trimethylsilyl) trifluoroacetamide) and FAME (fatty acid methyl esters) was added to each experimental sample and mixed quality control sample. After incubation with shaking at 70° C. for 56 minutes, the sample was transferred to an autosampler bottle with glass inserts. It was then injected into an Agilent 7890A gas chromatograph connected to a Pegasus HT time-of-flight mass spectrometer (Leco Corporation, St. Joseph, MI).

Example 4. DNA extraction and sequencing

To extract DNA from the bacterial extracellular vesicle membrane, the bacterial extracellular vesicle was boiled at 100°C for 40 minutes, and centrifuged at 13,000rpm at 4°C for 30 minutes to remove the remaining suspended particles and waste. A supernatant was obtained. DNA was extracted using the Dneasy PowerSoil kit (QIAGEN, Germany), and the standard protocol was performed according to the kit guide. DNA was quantified from extracellular vesicles of bacteria in each sample using the QIAxpert system (QIAGEN, Germany), and the bacterial genomic DNA was 16S_V3_F(5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3) specific to the V3-V4 hypervariable region of the 16S rDNA gene. ', SEQ ID NO: 1) and 16S_V4_R(5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC -3', SEQ ID NO: 2) primers. The library was prepared using PCR products according to the MiSeq system guide (Illumina, USA), and each amplicon was quantified, set at an equimolar ratio, and mixed, and the MiSeq platform (Illumina, USA) according to the manufacturer's instructions. The sequence was analyzed on the phase.

Example 5. Bioinformatics

Paired-end reads matching the adapter sequence were trimmed by cutadapt version 1.1.6. As a result, FASTQ files containing paired-end reads were merged with CASPER, and quality was filtered by Phred(Q) score based on the standard described by Bokulich. After merging, reads shorter than 350 bp or longer than 550 bp were also removed. To confirm the chimeric sequence, the reference-based chimera detection step was performed by VSEARCH against the SILVA gold database. Then, the sequence readout was clustered into Operational Taxonamic Units (OTU) using VSEARCH with a de novo clustering algorithm under the threshold of 97% sequence similarity, and the OTU containing one sequence in one sample was analyzed later. Was excluded. Representative sequences of OTU were finally classified using the SILVA 128 database with UCLUST (script on QIIME version 1.9.1), and normalization was applied to the data set using the total count method. The Chao index, which can assess the richness of each taxa, was assumed to measure the alpha diversity of each sample. The selection of metagenomic biomarkers for inclusion in the diagnostic model was based on relative abundance at the genus level. The criterion, fold-changes, and average relative abundance of false discovery rate (FDR)-adjusted p-values determined by the Wilcoxon test were less than 0.05, more than 2 times and more than 0.5%, respectively, in any group. In addition, a metabolite biomarker whose adjusted p-value was less than 0.05 and changed by more than 2 times was selected. All diagnostic models were calculated with logistic regression based on the Akaike information criteria using a stepwise selection method with a set of training and tests randomly selected at an 80:20 ratio. Performance values such as AUC, sensitivity, specificity, and accuracy were reported using a validation set. A logistic regression model was developed using respective omics data from metagenomic and metabolite biomarkers, and its accuracy was compared with the binding model of metagenomic and metabolite biomarkers to distinguish cancer from normal subjects.

Example 6. Statistics on metabolomics data

Multivariate and univariate analysis was performed using Metaboanalyst 4.0, and normalized data sets were analyzed using log transformation and pareto scaling, and principal component analysis (PCA) was used. Was carried out to investigate the differentiation of the overall metabolite profile between groups. Metabolite candidates were selected through univariate analysis using a false discovery rate (FDR)-adjusted p-value. Significant differences between the normal and colon cancer (CRC) patient groups were determined using the Wilcoxon test for continuous variables, and the results were considered significant if the p-value was less than 0.05.

Example 7. Statistical Analysis

Alpha diversity of microbial composition for richness and evenness was measured using Chao1 index and Shannon index to compare the species diversity of samples between normal and colorectal cancer patient groups. Relationships between samples were visualized using Bray-Curtis similarity-based principal coordinate analysis (PCoA) for beta diversity, and all statistical analyzes were performed using R version 3.5.1.

[Experimental Example]

Experimental Example 1. Metagenomic analysis of microbial-derived extracellular vesicles in stool samples of colon cancer patients and normal humans

Metagenomic (genetic) analysis was performed based on 16S rDNA amplicon sequencing to investigate the microbial composition of stool-derived extracellular vesicles from colon cancer patients and normal people, and 16S rDNA V3 and V4 as in Example 4 above. Sequencing data for the region was used to analyze the degree of rarefaction of 10,000 reads per sample.

1A shows the results of comparing the alpha diversity of colon cancer patients and normal people, and there was no significant difference based on Chao1 and Shannon indices. Whiskers in the box represent the range of minimum and maximum alpha diversity values within the population, excluding outliers.

1B shows beta diversity at the phylum and genus level through principal coordinate analysis (pCoA), and the red circle represents normal people and the blue circle represents colon cancer patients. Show. As a result of confirming the beta diversity, the comparison of beta diversity at the genus level showed a clear separation between groups compared to the phylum level cluster.

In addition, the relative changes in microbial frequency (abundance) at the level of the phylum (Fig. 2a) and genus (Fig. 2c) using heat maps are shown in Figs. 2a and 2c.

The heat map represents the relative abundance at the phylum level and genus level in colorectal cancer patients and normal subjects. Cells with a frequency value close to 0 are indicated in light blue, and cells greater than 0.5 are indicated in dark blue. The total frequency is 1.

The bar graphs shown in FIGS. 2B and 2D show statistically significant microbial composition based on the comparison at the level of the phylum (FIG. 2B) and the level of the genus (FIG. 2D) of colon cancer patients and normal persons. The gray bars represent the relative frequency of colorectal cancer patients, and the black bars represent the relative frequency of normal individuals (**p<0.05, ***p<0.01 between colon cancer patients and normal individuals).

As shown in FIG. 2B, Firmicutes significantly increased in colon cancer patients in comparison of the phylum level, while Proteobacteria decreased, and the microbial composition changed at the genus level of colon cancer patients compared to normal individuals. It is shown as a bar graph in 2d. Detailed records of the data are shown in Table 1 below.

Phylum	Genus	Normal person's MAV (mean abundance value) (%)	MAV in colorectal cancer patients (%)	Log2(Fold-change)	P-value (Wilcoxon)	FDR adjusted p-value (Wilcoxon)	regulation
Actinobacteria	Actinomyces	0.646	0.053	-3.613	0	0.001	Down
	Bifidobacterium	0.871	1.832	1.073	0.001	0.014	Up
	Rothia	0.533	0.016	-5.099	0	0	Down
	Propionibacterium	0.685	0.164	-2.061	0	0	Down
	Collinsella	0.065	0.953	3.88	0	0	Up
Bacteroidetes	Bacteroidales S24-7 group (f)	0.61	0.491	-0.313	0.003	0.031	Down
Cyanobacteria	Chloroplast (o)	0.507	0.059	-3.104	0	0	Down
Firmicutes	Blautia	0.187	0.599	1.677	0	0	Up
	Lachnoclostridium	0.096	0.74	2.943	0	0	Up
	LachnospiraceaeNK4A136 group	0.561	0.17	-1.718	0.002	0.019	Down
	LachnospiraceaeUCG-008	0.591	1.069	0.855	0	0	Up
	Dorea	0.45	0.51	0.181	0	0.008	Up
	[Eubacterium] coprostanoligenesgroup	1.332	5.696	2.096	0	0	Up
	RuminococcaceaeUCG-002	0.484	2.18	2.171	0	0.002	Up
	Ruminococcus 2	0.53	2.329	2.136	0	0.002	Up
	Subdoligranulum	0.562	2.408	2.099	0	0	Up
	Ruminococcaceae (f)	0.317	1.187	1.905	0	0	Up
	RuminococcaceaeUCG-014	1.098	0.706	-0.638	0	0.004	Down
	Faecalibacterium	4.624	11.974	1.373	0	0.003	Up
	RuminococcaceaeNK4A214 group	0.209	1.045	2.319	0.001	0.009	Up
	Staphylococcus	0.819	0.41	-0.998	0.003	0.033	Down
	Catenibacterium	0.041	0.729	4.161	0	0	Up
	Parvimonas	0.028	0.812	4.84	0.001	0.013	Up
	Ruminiclostridium 5	0.083	0.527	2.672	0.001	0.01	Up
Proteobacteria	Methylobacterium	2.846	0.027	-6.743	0	0.007	Down
	Solanum melongena (eggplant)	1.046	0.141	-2.887	0	0	Down
	Sphingomonas	0.548	0.172	-1.674	0	0	Down
	Diaphorobacter	0	0.974	12.397	0	0.001	Up
	Escherichia-Shigella	3.427	0.871	-1.975	0	0	Down
	Proteus	1.169	0	<<	0	0	Down
	Pseudomonas	2.913	0.242	-3.589	0	0	Down
	Enterobacter	0.158	0.816	2.37	0.004	0.035	Up
Saccharibacteria	Saccharibacteria (p)	0.54	0.132	-2.029	0.001	0.017	Down
Tenericutes	Mollicutes RF9 (o)	2.053	0.208	-3.304	0.001	0.011	Down

As shown in Table 1, significant differences were observed in 34 genus bacteria between colon cancer and normal people. Among them, Actinomyces, Rothia, Propionibacterium, Bacteroidiales S24-7 group (f), Chloroplast (o), Lachnospiraceae NK4A136 group, Ruminococcaceae UCG-014, Staphylococcus, Methylobacterium, Solanum melongena, Sphingomonas, Escherichia-shigella, Proteus, Pseudomonas, Saccharibacteria ( p), And Mollicutes RF9 (o) genus bacteria decreased in colorectal cancer patients compared to normal subjects (p<0.05), whereas Bifidobacterium , Collinsella , Blautia , Lachnoclostridium , Lachnospiraceae UCG-008 , Dorea , Eubacterium coprostanoligenes group , Ruminococcus 2 , Faecalibacterium , Ruminococcaceae NK4A214 group , Ruminococcaceae UCG-002 , Subdoligranulum , Ruminococcaceae (f), Catenibacterium , Parvimonas , Ruminiclostridium 5 , Enterobacter , and Diaphorobacter genus The proportion of bacteria significantly increased (p<0.05).

In particular, in the case of Firmicutes , except for the composition of Lachnospiraceae NK4A136 group , Ruminococcaceae UCG-014 , and Staphylococcus , all increased in colon cancer patients, and in the case of Proteobacteria , the composition decreased in all colon cancer patients except Diaphorobacter and Enterobacter. In addition, Proteus spp of Proteobacteria. Was markedly changed in colorectal cancer patients and did not exist in normal subjects. Taxonomic profiles of microbial-derived extracellular vesicles in colon cancer patients and normal people are shown in Figs. 3a (phylum) and 3b (genus).

3A and 3B are analysis data performed on 16S rDNA V3 and V4 region data, and the degree of rarefaction of 10,000 reads per sample was analyzed. At the phylum level (Fig. 3a) and genus level (Fig. 3b), the relative taxonomic frequencies for colorectal cancer patients and normal persons are indicated, and each individual is indicated along the horizontal axis, and the relative classification frequency is indicated on the vertical axis. Is displayed.

Experimental Example 2. Metabolite analysis of extracellular vesicles derived from feces in colon cancer patients and normal humans

In order to evaluate the properties of small molecule metabolites in microbial-derived extracellular vesicles, a global gas chromatography time-of-flight mass spectrometry was used as the method of Example 3 above. ) As a result of performing metabolite analysis, three PCs (PC1, PC2, PC3) in the three-dimensional PCA score graph as shown in FIG. 4A clearly separated the metabolic profiles of colon cancer patients and normal people.

In FIG. 4A, a score plot of three-dimensional principal component analysis (PCA) shows the metabolic pattern of fecal extracellular vesicles (EV) samples. The red circle represents colorectal cancer and the green circle represents a normal person (PC, principal component).

Metabolites identified by multivariate analysis were selected according to the Q-value, the p-value adjusted for FDR. Metabolites that were statistically significant (Q <0.05) are shown in Table 2 below.

4B shows loading plots of PC1 and PC2 from PCA results of metabolites that are differentially accumulated from colon cancer patients versus normal individuals, and the loading plot shows metabolites that effectively distinguish colon cancer patients from normal individuals. Showed.

Among the metabolites shown in Table 2, amino acids had the highest ratio, and most of them were upregulated in colon cancer patients. In addition, alcohol forms (ethanolamine and phenol), carboxylic acids (Furoic acid, succinic acid, and oxalic acid), and fatty acids (hexanoic acid ), palmitic acid, and oleic acid) were improved in colorectal cancer patients compared to normal subjects, and bacterial metabolites such as aminoisobutyric acid and butanoic acid were decreased. .

Experimental Example 3. Association between microbial and metabolic profiles in fecal-derived extracellular vesicles

Pearson rank association analysis showed a close correlation between intestinal microbiota and specific metabolites.

Figure 5a shows the results of investigating the association between metagenomic and metabolite analysis data by performing Pearson's association analysis.The y-axis shows the metagenomic analysis results of bacterial-derived extracellular vesicles obtained through statistical comparison, and the x-axis metabolism Sieve biomarkers are shown. Each square represents a correlation coefficient value. Red squares represent positive correlations and blue squares represent negative correlations between microbial and metabolite frequencies.

As shown in FIG. 5A, the relative frequencies of most metabolic markers had a large positive correlation with genus bacteria belonging to Firmicutes. In particular, several amino acids were increased according to the consistent regulation of the intestinal microbiota of colon cancer patients, and these bacteria had a significant relationship with tyramine, phenol, and hexanoic acid (r >| 0.5|, P <0.05). On the other hand, bacteria belonging to Proteobacteria had a negative correlation with metabolic markers, and the observation of Proteobacteria was opposite to that of Firmicutes (r<|0.5|, P<0.05). Among metabolite biomarkers, carboxylic acids (furoic acid, succinic acid, oxalic acid) and long-chain fatty acids (palmitic acid, oleic acid) were not significantly correlated with the overall intestinal microbial level.

Experimental Example 4. Colon cancer diagnosis model based on microbial and metabolic profiles in fecal-derived extracellular vesicles

In order to further define useful biomarkers from metagenomic and metabolite biomarkers, a biomarker capable of distinguishing colon cancer-positive individuals from normal individuals using an optimized algorithm of binary logistic regression analysis and forward step-by-step method. An optimal model was constructed as a marker, and as a result, two metabolites (leucine and oxalic acid) and two genus bacteria ( Colinsella ) And Solanum melongena ) derived extracellular vesicles were selected.

Figure 5b shows the receiver operation characteristic (ROC) curve of the logistic regression model for leucine, oxalic acid and Collinsella , Solanum melongena markers, which is based on the ability to distinguish colorectal cancer patients from normal subjects. The red line represents the metabolomics-based model, the blue line represents the metagenomics-based model, and the green line represents the model based on a combination of metabolomics and metagenomics.

By confirming the ROC curve of the logistic regression model shown in FIG. 5B, colon cancer-positive samples were distinguished from normal individuals using the selected biomarker.

As a result, when the two metabolite biomarkers were used, the predictability of colon cancer was 92.0% with a sensitivity of 80.0% and a specificity of 100%, and the two selected metagenomic biomarkers were The AUC value (95.0%) was slightly higher with a sensitivity of 90.0% and a specificity of 100%.

In addition, when the two ohmic data panels were combined, the AUC was found to be 100% as a related accuracy in distinguishing between a positive colorectal cancer sample and a normal person, and the training set (left drawing of FIG. 5B) and the test set (FIG. 5B of FIG. There was no significant difference between the figures on the right). The higher the AUC value, the higher the ratio of the normal person to the normal person and the colorectal cancer patient to properly judge the colorectal cancer. Therefore, the combination of the markers having the highest AUC means the highest diagnostic significance.

Accordingly, from these data, it was expected that a potential representative biomarker of colon cancer, which is a combination of metagenomic and metabolite biomarkers, will diagnose colorectal cancer more accurately than a single ohmic biomarker.

In addition, from the above results, there is a strong association between the frequency of intestinal microorganisms (Firmicutes and Proteobacteria ) and metabolites (mainly amino acids), which is due to the altered composition of macronutrient fermentation and decomposition bacteria in colon cancer. It was found that it could lead to depletion. In addition, it was found that the extracellular vesicles secreted by the intestinal microbes transmit metabolic information in a dynamic range that reflects the nutritional status, metabolism, and immune response of the host in the presence of a disease.

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

The method for diagnosing colorectal cancer according to the present invention is expected to be useful for early diagnosis and prediction of colorectal cancer through metagenomic analysis of extracellular vesicles secreted from intestinal bacteria and analysis of metabolites in extracellular vesicles.

Claims (12)

1. Information providing method for diagnosis of colorectal cancer, comprising the following steps:

   (a) separating the normal person and the subject sample;

   (b) separating extracellular vesicles from the sample;

   (c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

   (d) comparing the increase or decrease of the content of metabolites through metabolite analysis in the isolated extracellular vesicles.
2. The method of claim 1,

   The sample is characterized in that the feces, information providing method for colon cancer diagnosis.
3. The method of claim 1,

   The metabolite is characterized in that at least one selected from the group consisting of amino acids, amino alcohols, aromatic alcohols, carboxylic acids, and fatty acids. How to provide information for cancer diagnosis.
4. The method of claim 1,

   In the step (c), the extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of Firmicutes and Proteobacteria, or

   My solution Tino access (Actinomyces), Bifidobacterium (Bifidobacterium), Loti Ah (Rothia), propionic sludge tumefaciens (Propionibacterium), Colin Cellar (Collinsella), Les night interrogating two months (Bacteroidales) S24-7 group (f) , chloro plast (Chloroplast) (o), Blau thiazole (Blautia), La pants Clostridium (Lachnoclostridium), La pants at Spira (Lachnospiraceae) NK4A136 when the group La pants Spira (Lachnospiraceae) UCG-008, Dorea , Eubacterium coprostanoligenes group, Ruminococcaceae UCG-002 , Ruminococcus 2 , Subdoligranulum , Rumi Ruminococcaceae (f), Ruminococcaceae UCG-014 , Faecalibacterium , Ruminococcaceae NK4A214 group, Staphylococcus , Ka I'll tumefaciens (Catenibacterium), Parr ratio Pseudomonas (Parvimonas), Rumi Nicklaus tree Stadium (Ruminiclostridium) 5, methyl tumefaciens (Methylobacterium), Solar titanium Mello dehydrogenase (Solanum Melongena), Sphingomonas (Sphingomonas), dia captive bakteo (Diaphorobacter), Escherichia - Shigella (Escherichia-shigella), Proteus (Proteus), Pseudomonas (Pseudomonas), Enterobacter (Enterobacter), four Kari bacteria (Saccharibacteria) (p), and the molar Riku Tess (Mollicutes) RF9 to (o) A method for providing information for diagnosing colon cancer, characterized in that the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of, is compared.
5. The method of claim 4,

   In the step (c), compared to a normal person, Firmicutes phylum bacterium-derived extracellular vesicles, or

   Bifidobacterium (Bifidobacterium), Colin Cellar (Collinsella), Blau Tia (Blautia), La Chino Clostridium (Lachnoclostridium), at La Chino Spira (Lachnospiraceae) UCG-00 8, Dore Ah (Dorea), Aust at tumefaciens Cope star fun I Ness (Eubacterium coprostanoligenes) group, luminometer coca to (Ruminococcaceae) UCG-002, luminometer Caucus (Ruminococcus) at 2, sub-turn Gras nulreom (Subdoligranulum), luminometer coca (Ruminococcaceae) (f), Faecalibacterium , Ruminococcaceae NK4A214 group, Catenibacterium , Parvimonas , Ruminiclostridium 5 , Diaphorobacter ( Diaphorobacter ), and Enterobacter ( Enterobacter ), characterized in that when the content of extracellular vesicles derived from one or more genus bacteria selected from the group consisting of is increased, the risk of colon cancer is predicted to be high, colon How to provide information for cancer diagnosis.
6. The method of claim 4,

   In the step (c), compared to a normal person, proteobacteria ( Proteobacteria ) phylum bacteria-derived extracellular vesicles, or

   Actinomyces , Rothia , Propionibacterium , Bacteroidales S24-7 group (f), Chloroplast (o), Lacinospira Lachnospiraceae NK4A136 group, Ruminococcaceae UCG-014 , Staphylococcus , Methylobacterium , Solanum melongena , Sphingomonas , Escherichia - Shigella (Escherichia-shigella), Proteus (Proteus), Pseudomonas (Pseudomonas), four Kariya bacteria (Saccharibacteria) (p), and the mole Riku test (Mollicutes) is selected from the group consisting of RF9 (o) A method for providing information for diagnosis of colon cancer, characterized in that it is predicted that the risk of developing colon cancer is high when the content of one or more genus bacteria-derived extracellular vesicles is reduced.
7. The method of claim 1,

   In the step (d), leucine, isoleucine, alanine, lysine, tyramine, aminoisobutyric acid, ethanolamine, phenol, Group consisting of furoic acid, succinic acid, oxalic acid, butanoic acid, hexanoic acid, palmitic acid, and oleic acid Comparing the increase or decrease in the content of one or more metabolites selected from, a method for providing information for colon cancer diagnosis.
8. The method of claim 7,

   Compared to the normal person in the step (d), Leucine, Isoleucine, Alanine, Lysine, Tyramine, Ethanolamine, Phenol, Furoic acid ), succinic acid, oxalic acid, hexalic acid, hexanoic acid, palmitic acid, and oleic acid. If present, the method for providing information for the diagnosis of colon cancer, characterized in that predicting that the risk of developing colon cancer will be high.
9. The method of claim 7,

   In the step (d), when the content of aminoisobutyric acid or butanoic acid is decreased compared to the normal person, the risk of developing colon cancer is predicted to be high. How to provide information.
10. The method of claim 1,

    In the step (c), the content of extracellular vesicles derived from bacteria of the genus Collinsella is increased and the content of extracellular vesicles derived from bacteria of the genus of Solanum melongena is decreased compared to a normal person. And

    In the step (d), when leucine and oxalic acid are increased compared to normal people, the risk of developing colon cancer is predicted to be high.
11. Information providing method for predicting the onset of colon cancer, comprising the following steps:

    (a) separating the normal person and the subject sample;

    (b) separating extracellular vesicles from the sample;

    (c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

    (d) comparing the increase or decrease of the content of metabolites through metabolite analysis in the isolated extracellular vesicles.
12. A method for diagnosing colorectal cancer, comprising the following steps:

    (a) separating the normal person and the subject sample;

    (b) separating extracellular vesicles from the sample;

    (c) extracting DNA from the isolated extracellular vesicles and comparing the increase or decrease in the content of bacterial-derived extracellular vesicles through metagenomic analysis; And

    (d) comparing the increase or decrease of the content of metabolites through metabolite analysis in the isolated extracellular vesicles.

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