KR20190003330A - Method for diagnosis of lung cancer in asthma patients using analysis of bacteria metagenome - Google Patents

Abstract

The present invention relates to a method for diagnosing lung cancer by using bacteria metagenome analysis and, specifically, to a method for diagnosing lung cancer by analyzing any increase or decrease of content of extracellular vesicles derived from specific bacteria by performing bacteria metagenome analysis using a sample derived from a subject to be tested. The extracellular vesicles secreted from bacteria existing in the environment are absorbed in human body and directly affect the development of cancer. Lung cancer is difficult to diagnose at the early stages before symptoms become apparent, which makes lung cancer difficult to treat. Therefore, according to the present invention, the method for diagnosing lung cancer by using bacteria metagenome can prevent or delay the occurrence of the disease by properly managing high risk groups of lung cancer patients through early diagnosis and prediction by predicting the risk degree for lung cancer by analyzing the bacteria metagenome about genes existing in the extracellular vesicles derived from bacteria using the sample derived from the human body.

Description

[0001] The present invention relates to a method for diagnosing lung cancer by analyzing a bacterial meta-genome in asthmatic patients,

The present invention relates to a method for diagnosing lung cancer through the analysis of bacterial metagenomes, and more specifically, by analyzing a bacterial metagenome using a sample derived from a subject, analyzing the increase or decrease of the content of the extracellular vesicles derived from a specific bacterium, And the like.

Lung cancer is a malignant tumor originating from the lungs, and despite the development of modern medicine, it is the leading cause of death in Korea, with a 5 - year survival rate of less than 50%. Lung cancer is divided into small cell lung cancer and non-small cell lung cancer depending on the type of tissue, and the small cell lung cancer is distinct from other types of lung cancer in terms of treatment and prognosis Since lung cancer has a histologic examination result, it is very important to determine the treatment policy.

Lung cancer usually presents with symptoms such as coughing, hemoptysis, chest pain, and shortness of breath. However, when symptoms are present, they are already progressing, and even if they are ongoing, they are not symptomatic. Only 5-15% If you do not have a diagnosis, you will be diagnosed with lung cancer after most symptoms. There is no known screening method for early detection of lung cancer before the onset of symptoms. However, studies on early detection of lung cancer using computed tomography (CT) of the chest area have been actively conducted, It was not proven. Therefore, it is urgent to develop a method to diagnose lung cancer early and to improve the treatment efficiency. It is very important to differentiate the early diagnosis and treatment methods by predicting the onset of lung cancer in advance. Research and technology development is required.

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

It has long been known that chronic inflammation causes cancer. In recent years, colon cancer has been reported to be caused by the Th17 immune response to toxins derived from intestinal bacteria. Helicobacter pylori, Of gastric cancer. Recently, inflammatory airway diseases including asthma are known to be an important risk factor for the development of lung cancer. Recently, the incidence of lung cancer in non - smokers has increased, suggesting that there is a causative factor in lung cancer development besides smoking.

Metagenomics, also called environmental genomics, can be said to be an analysis of metagenomic data obtained from samples taken in the environment (Korean Patent Laid-Open Patent No. 2011-073049). Recently, it has become possible to catalog the bacterial composition of human microbial germs based on the 16s ribosomal RNA (16s rRNA) base sequence, and it is possible to sequence the bacterial sequence through the next generation sequencing (NGS) platform Analyze. However, there has been no report on a method for identifying causative factors of lung cancer and predicting lung cancer through analysis of metagenomes present in bacterial-derived vesicles in the human body such as blood of asthmatic patients.

In order to diagnose lung cancer in asthmatic patients, the gene was extracted from the blood of the subject, and the analysis of the bacterial meta genome was performed on the extracted gene. As a result, the blood of the lung cancer patient was significantly increased Derived bovine vesicle was identified. Based on this finding, the present invention was completed.

Accordingly, it is an object of the present invention to provide an information providing method for diagnosing lung cancer in a patient suffering from asthma, a method for diagnosing lung cancer, and a method for predicting the risk of lung cancer through metagenome analysis of extracellular vesicles derived from bacteria.

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

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

(a) extracting DNA from extracellular vesicles isolated from asthmatic patients and subjects;

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

(c) comparing the increase or decrease in the content of the bacterial-derived extracellular vesicles with the sample derived from the asthmatic patients through sequence analysis of the PCR product.

The present invention also provides a method of diagnosing lung cancer, comprising the steps of:

(a) extracting DNA from extracellular vesicles isolated from asthmatic patients and subjects;

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

(c) comparing the increase or decrease in the content of the bacterial-derived extracellular vesicles with the sample derived from the asthmatic patients through sequence analysis of the PCR product.

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

(a) extracting DNA from extracellular vesicles isolated from asthmatic patients and subjects;

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

(c) comparing the increase or decrease in the content of the bacterial-derived extracellular vesicles with the sample derived from the asthmatic patients through sequence analysis of the PCR product.

In one embodiment of the present invention, the asthmatic patient and the subject sample are blood,

In step (c), Bacteroidetes, Cyanobacteria, TM7, Fusobacteria, Thermi, Verrucomicrobia, Armatimonadetes, Extracellular vesicles derived from one or more phylum bacteria selected from the group consisting of Acidobacteria, Gemmatimonadetes, and Chloroflexi,

Bacteroidia, Bacilli, Flavobacteriia, Sphingobacterias, Alphaproteobacteria, Fusobacterias, TM7-3, Deinococci, , Verrucomicrobiae, Saprospirae, Chloroplast, Cytophagia, Fimbriimonadia, Chloracidobacteria, Thermomicrobia, Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Thermomicrobia, Thermoleophilia, and Solibacteres,

YS2, Turicibacterales, Bifidobacteriales, Bacteroidales, Enterobacteriales, Rhodobacterales, Gemellales, Plasmodium, For example, Flavobacteriales, Caulobacterales, Neisseriales, Sphingobacteriales, Deinococcales, Pseudomonadales, Rhodocyclameridae, For example, Rhodocyclales, Xanthomonadales, Fusobacteriales, Actinomycetales, Sphingomonadales, Verrucomicrobiales, Such as Saprospirales, Rhizobiales, Bacillales, Streptophyta, Cytophagales, Thermales, Fibromaradales (F imbriimonadales, CW040, Rickettsiales, RB41, Alteromonadales, JG30-KF-CM45, I025, Aeromonadales, and Solibacterales. One or more orders of germ-derived extracellular vesicles selected from the group,

Such as Helicobacteraceae, Bacteroidaceae, Turicibacteraceae, Veillonellaceae, Bifidobacteriaceae, Barnées lacrosis, Bacillus thuringiensis, But are not limited to, Barnesiellaceae, Rikenellaceae, Clostridiaceae, Odoribacteraceae, Enterobacteriaceae, Porphyromonadaceae, Gemellaceae, Weeksellaceae, Carnobacteriaceae, Leptotrichiaceae, Moraxellaceae, Caulobacteraceae, Erythobacteriaceae, For example, Erythrobacteraceae, Hyphomicrobiaceae, Neisseriaceae, Sphingobacteriaceae, Deinococcaceae, Aerococcaceae, Bato Nelashi But are not limited to, bacteria such as Bartonellaceae, Micrococcaceae, Flavobacteriaceae, Burkholderiaceae, Lactobacillaceae, Dietziaceae, For example, Rhodocyclaceae, Xanthomonadaceae, Geodermatophilaceae, Actinomycetaceae, Methylobacteriaceae, Pseudomonadaceae, For example, Corynebacteriaceae, Staphylococcaceae, Nocardioidaceae, Verrucomicrobiaceae, Sphingomonadaceae, Mycobacteriaceae, But are not limited to, Mycobacteriaceae, Tissierellaceae, Chitinophagaceae, Intrasporangiaceae, Propionibacteriaceae, (Aurantimonadaceae), Planococcaceae, Fusobacteriaceae, Bradyrhizobiaceae, Nocardiaceae, Dermabacteraceae, and the like. , Bacillaceae, Thermaceae, Ellin 6075, Brevibacteriaceae, Microbacteriaceae, Rhodospirillaceae, Cytopagasia, (Eg, Cytophagaceae, Fimbriimonadaceae, Dermacoccaceae, Chromatiaceae, Rhizobiaceae, Gordoniaceae, mitochondria, Selected from the group consisting of Pseudonocardiaceae, Exiguobacteraceae, Shewanellaceae, F16, Rs-045, and Aeromonadaceae. One or more family bacteria Derived extracellular vesicles, or

But are not limited to, Trabulsiella, Enterobacter, Veillonella, Bifidobacterium, Lachnospira, Comamonas, Bacteroides, Such as Turicibacter, Sutterella, Klebsiella, SMB53, Roseburia, Dialister, Ruminococcus, Parabacteroides, But are not limited to, Butyricimonas, Odoribacter, Eubacterium, Dorea, Enhydrobacter, Granulicatella, Chryseobacterium, Such as Porphyromonas, Coprococcus, Peptoniphilus, Microbispora, Deinococcus, Acinetobacter, Aerococcus, Actinomyces, Actinomyces, Brev undimonas, Blastomonas, Citrobacter, Lactobacillus, Stenotrophomonas, Corynebacterium, Pseudomonas, Lautropia, Lactobacillus, ), Akkermansia, Staphylococcus, Bacillus, Sphingobacterium, Anaerococcus, Neisseria, Leptotrichia, Myco But are not limited to, Mycobacteria, Mycobacterium, Kocuria, Methylobacterium, Propionibacterium, Hymenobacter, Sphingomonas, Fusobacterium, Such as Brachybacterium, Rhodococcus, Micrococcus, Kaistobacter, Finegoldia, Rubellimicrobium, Brevibacterium, Agrobacterium, But are not limited to, Agrobacterium, Dietzia, Fimbriimonas, Flavobacterium, Dermacoccus, Skermanella, Novosphingobium, Gordonia, Rheinheimera, Achromobacter, Hydrogenophilus, Thermus, Exiguobacterium, Shewanella, Ralstonia, Ralstonia), and Alkanindiges. The amount of the extracellular vesicles derived from one or more genus bacterial cells selected from the group consisting of Alkanindiges can be compared.

In another embodiment of the present invention, in the step (c), as compared with a sample derived from an asthmatic patient,

Cyanobacteria, TM7, Fusobacteria, Thermi, Verrucomicrobia, Armatimonadetes, Acidobacteria, Gematimonadetes ( Gemmatimonadetes), and Chloroflexi (Phylum Bacteria), as well as one or more phylum bacterial extracellular vesicles,

Bacilli, Flavobacteriia, Sphingobacteriia, Alphaproteobacteria, Fusobacterias, TM7-3, Deinococci, Berukomicrobiere (Bacilli, Such as Verrucomicrobiae, Saprospirae, Chloroplast, Cytophagia, Fimbriimonadia, Chloracidobacteria, Thermomicrobia, Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Thermoleophilia, Solibacteres,

But are not limited to, Rhodobacterales, Gemellales, Flavobacteriales, Caulobacterales, Neisseriales, Sphingobacteriales, And the like, such as Deinococcales, Pseudomonadales, Rhodocyclales, Xanthomonadales, Fusobacteriales, Actinomycetales, Sphingomonadales Sphingomonadales, Verrucomicrobiales, Saprospirales, Rhizobiales, Bacillales, Streptophyta, Cytophagales, Thermales (including, for example, Thermales, Fimbriimonadales, CW040, Rickettsiales, RB41, Alteromonadales, JG30-KF-CM45, I025, Aeromonadales, and Soli night One or more order bacterial extracellular vesicles selected from the group consisting of Solibacterales,

Such as Gemellaceae, Weeksellaceae, Carnobacteriaceae, Leptotrichiaceae, Moraxellaceae, Caulobacteraceae, Erythobacteria, Neomycetes, such as Erythrobacteraceae, Hyphomicrobiaceae, Neisseriaceae, Sphingobacteriaceae, Deinococcaceae, Aerococcaceae, , Bartonellaceae, Micrococcaceae, Flavobacteriaceae, Burkholderiaceae, Lactobacillaceae, Dietziaceae, and the like. Rhodocyclaceae, Xanthomonadaceae, Geodermatophilaceae, Actinomycetaceae, Methylobacteriaceae, Pseudomonas aeruginosa, Pseudomonas aeruginosa, Pseudomonadaceae, Corynebacteriaceae, Staphylococcaceae, Nocardioidaceae, Verrucomicrobiaceae, Sphingomonadaceae, Pseudomonadaceae, Corynebacteriaceae, , Mycobacteriaceae, Tissierellaceae, Chitinophagaceae, Intrasporangiaceae, Propionibacteriaceae, Aurantimonaceae, For example, Aurantimonadaceae, Planococcaceae, Fusobacteriaceae, Bradyrhizobiaceae, Nocardiaceae, Dermabacteraceae, Bacillaceae, Thermaceae, Ellin 6075, Brevibacteriaceae, Microbacteriaceae, Rhodospirillaceae, Cytopara, (Eg, Cytophagaceae, Fimbriimonadaceae, Dermacoccaceae, Chromatiaceae, Rhizobiaceae, Gordoniaceae, mitochondria, Selected from the group consisting of Pseudonocardiaceae, Exiguobacteraceae, Shewanellaceae, F16, Rs-045, and Aeromonadaceae. One or more family bacterial-derived extracellular vesicles, or

Euroacterium, Dorea, Enhydrobacter, Granulicatella, Chryseobacterium, Porphyromonas, Coprococcus, But are not limited to, Peptoniphilus, Microbispora, Deinococcus, Acinetobacter, Aerococcus, Actinomyces, Brevundimonas, But are not limited to, Blastomonas, Citrobacter, Lactobacillus, Stenotrophomonas, Corynebacterium, Pseudomonas, Lautropia, The present invention relates to the use of the compounds of the formula (I) for the preparation of a medicament for the treatment of a disease or condition selected from the group consisting of Akkermansia, Staphylococcus, Bacillus, Sphingobacterium, Anaerococcus, Neisseria, Leptotrichia, Mycobacterium ), Kocuria, Methylobacterium, Propionibacterium, Hymenobacter, Sphingomonas, Fusobacterium, Brkibacterium, Brachybacterium, Rhodococcus, Micrococcus, Kaistobacter, Finegoldia, Rubellimicrobium, Brevibacterium, Agrobacterium, Agrobacterium, Fimbriimonas, Flavobacterium, Dermacoccus, Skermanella, Novosphingobium, Gordonia, Pseudomonas aeruginosa, Dietzia, Fimbriimonas, Flavobacterium, Rheinheimera, Achromobacter, Hydrogenophilus, Thermus, Exiguobacterium, Shewanella, Ralstonia, and the like. Alkan indiges) can be diagnosed as lung cancer when the content of one or more genus bacterial-derived extracellular vesicles is increased.

In another embodiment of the present invention, in the step (c), as compared with a sample derived from an asthmatic patient,

Bacteroidetes (phylum) Bacterial-derived extracellular vesicles,

Bacteroidia class Bacterial-derived extracellular vesicles,

One or more order bacterium-derived cells selected from the group consisting of YS2, Turicibacterales, Bifidobacteriales, Bacteroidales, and Enterobacteriales. Outside parcels,

Such as Helicobacteraceae, Bacteroidaceae, Turicibacteraceae, Veillonellaceae, Bifidobacteriaceae, Barnées lacrosis, Bacillus thuringiensis, , Which is composed of Barnesiellaceae, Rikenellaceae, Clostridiaceae, Odoribacteraceae, Enterobacteriaceae, and Porphyromonadaceae. One or more family bacterial-derived extracellular vesicles selected from the group consisting of

But are not limited to, Trabulsiella, Enterobacter, Veillonella, Bifidobacterium, Lachnospira, Comamonas, Bacteroides, Such as Turicibacter, Sutterella, Klebsiella, SMB53, Roseburia, Dialister, Ruminococcus, Parabacteroides, Lung cancer can be diagnosed when the content of at least one genus bacterium-derived extracellular vesicle selected from the group consisting of Butyricimonas and Odoribacter is decreased.

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

In another embodiment of the present invention, in step (c), Bacteroidetes, Cyanobacteria, TM7, Fusobacteria, Thermi, Verrucomicrobia, From one or more phylum bacteria-derived extracellular vesicles selected from the group consisting of Armatumonadetes, Acidobacteria, Gemmatimonadetes, and Chloroflexi. Can be compared.

In another embodiment of the present invention, in step (c), at least one selected from the group consisting of Bacteroidia, Bacilli, Flavobacteriia, Sphingobacteriia, Alphaproteobacteria, Such as Fusobacterias, TM7-3, Deinococci, Verrucomicrobiae, Saprospirae, Chloroplast, Cytophagia, Pimbrimonidia, Derived from one or more classes of bacterial cells selected from the group consisting of Fimbriimonadia, Chloracidobacteria, Thermomicrobia, Thermoleophilia, and Solibacteres. It is possible to compare the increase and decrease of the contents of vesicles.

In another embodiment of the present invention, in step (c), YS2, Turicibacterales, Bifidobacteriales, Bacteroidales, Enterobacteriales, Rhodobacteria, But are not limited to, Rhodobacterales, Gemellales, Flavobacteriales, Caulobacterales, Neisseriales, Sphingobacteriales, Such as Deinococcales, Pseudomonadales, Rhodocyclales, Xanthomonadales, Fusobacteriales, Actinomycetales, Sphingomonadales, ), Verrucomicrobiales, Saprospirales, Rhizobiales, Bacillales, Streptophyta, Cyt < RTI ID = 0.0 > ophagales, Thermales, Fimbriimonadales, CW040, Rickettsiales, RB41, Alteromonadales, JG30-KF-CM45, I025, Aeromonadales, and Solibacterales can be compared in order to compare the increase and decrease of the content of one or more kinds of extracellular vesicles derived from an order bacterium.

In yet another embodiment of the present invention, in step (c), there is provided a method of screening for Helicobacteraceae, Bacteroidaceae, Turicibacteraceae, Veillonellaceae ), Bifidobacteriaceae, Barnesiellaceae, Rikenellaceae, Clostridiaceae, Odoribacteraceae, Enterobacteriaceae, Bifidobacteriaceae, Barnesiellaceae, Pseudomonas aeruginosa, Enterobacteriaceae, Porphyromonadaceae, Gemellaceae, Weeksellaceae, Carnobacteriaceae, Leptotrichiaceae, Moraxellaceae, (Eg, Caulobacteraceae, Erythrobacteraceae, Hyphomicrobiaceae, Neisseriaceae, Sphingobacteriaceae, Deinocokeia, (Deinococ clavulanic acid, cacao, Aerococcaceae, Bartonellaceae, Micrococcaceae, Flavobacteriaceae, Burkholderiaceae, Lactobacillus, But are not limited to, Lactobacillaceae, Dietziaceae, Rhodocyclaceae, Xanthomonadaceae, Geodermatophilaceae, Actinomycetaceae, Methylobacteriaceae, Pseudomonadaceae, Corynebacteriaceae, Staphylococcaceae, Nocardioidaceae, Berukomicrobiacea, and the like. For example, in the order of Verrucomicrobiaceae, Sphingomonadaceae, Mycobacteriaceae, Tissierellaceae, Chitinophagaceae, Intrasporangiaceae, In addition to the naturally occurring bacteria such as Propionibacteriaceae, Aurantimonadaceae, Planococcaceae, Fusobacteriaceae, Bradyrhizobiaceae, Nocardiaceae, Nocardiaceae, Dermabacteraceae, Bacillaceae, Thermaceae, Ellin 6075, Brevibacteriaceae, Microbacteriaceae, Such as Rhodospirillaceae, Cytophagaceae, Fimbriimonadaceae, Dermacoccaceae, Chromatiaceae, Rhizobiaceae, Gordonii, (Gordoniaceae), mitochondria, Pseudonocardiaceae, Exiguobacteraceae, Shewanellaceae, F16, Rs-045, and AE Made up of the Aeromonadaceae The increase or decrease in the content of one or more kinds of extracellular vesicles derived from one or more family members selected from the group can be compared.

In another embodiment of the present invention, in the step (c), in addition to the above-mentioned step (a), the step (b) is carried out in the order of Trabulsiella, Enterobacter, Veillonella, Bifidobacterium, Lachnospira, Such as Comamonas, Bacteroides, Turicibacter, Sutterella, Klebsiella, SMB53, Roseburia, Dialister, But are not limited to, Ruminococcus, Parabacteroides, Butyricimonas, Odoribacter, Eubacterium, Dorea, Enhydrobacter, Granulicatella, Chryseobacterium, Porphyromonas, Coprococcus, Peptoniphilus, Microbispora, Deinococcus, Acinetobacter, (Acinetobacter), Aerococcus, Actinomyces, Brevundimonas, Blastomonas, Citrobacter, Lactobacillus, Stenotrophomonas, Corynebacterium, Corynebacterium, Corynebacterium, Pseudomonas, Lautropia, Akkermansia, Staphylococcus, Bacillus, Sphingobacterium, Anaerococcus, Nexia, and so on. Neisseria, Leptotrichia, Mycobacterium, Kocuria, Methylobacterium, Propionibacterium, Hymenobacter, Sphingomonas, Sphingomonas, Fusobacterium, Brachybacterium, Rhodococcus, Micrococcus, Kaistobacter, Finegoldia, Rubell such as, for example, imicrobium, Brevibacterium, Agrobacterium, Dietzia, Fimbriimonas, Flavobacterium, Dermacoccus, Such as Skermanella, Novosphingobium, Gordonia, Rheinheimera, Achromobacter, Hydrogenophilus, Thermus, Exiguobacterium, Extracellular vesicles derived from one or more genus bacterial cells selected from the group consisting of Alzheimer's disease, Alzheimer's disease, Shewanella, Ralstonia, and Alkanindiges.

The extracellular vesicles secreted from the germs present in the environment are absorbed into the body and can directly affect the inflammatory airway diseases such as asthma and cancer, and lung cancer is difficult to be efficiently treated because it is difficult to diagnose it before the symptoms occur. Thus, by analyzing metagenomes of bacterial or germ-derived extracellular vesicles using samples derived from human body according to the present invention, it is possible to predict early risk of lung cancer by predicting the risk of lung cancer in asthmatic patients, Can slow the onset of the disease or prevent the onset. In addition, early diagnosis can be performed even after the onset, so that the incidence of lung cancer can be lowered and the therapeutic effect can be enhanced. In addition, the metagenome analysis in patients diagnosed with lung cancer predicts causative factors, And it is possible to prevent recurrence of the disease.

1A and 1B are views for evaluating the distribution pattern of extracellular vesicles derived from bacteria in the body. FIG. 1A is a graph showing the distribution of intracellular vesicles derived from bacteria (Bacteria) and bacterial-derived vesicles (EV) FIG. 1B is a photograph of the distribution pattern of the cells in the blood and various organs (3h, 6h, and 12h) after oral administration of enteric bacteria (Bacteria) and extracellular vesicles (EV) Heart, lung, liver, kidney, spleen, adipose tissue, and muscle), and the distribution patterns of the bacteria and extracellular vesicles are photographed.
FIG. 2 shows the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at the phylum level by performing a metagenome analysis after separating bacterial-derived vesicles from patients with lung cancer and asthma.
FIG. 3 shows the distribution of bacterial-derived vesicles (EVs), which have diagnostic performance at the class level, by performing the metagenome analysis after separating bacterial-derived vesicles from the blood of patients with lung cancer and asthma.
FIG. 4 shows the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at the order level by performing a metagenome analysis after separating bacterial-derived vesicles from the blood of patients with lung cancer and asthma.
FIG. 5 shows the distribution of bacterial-derived vesicles (EVs) in the blood of lung cancer patients and asthmatic patients after bacterial-derived vesicles were separated from the blood of the patients suffering from asthma.
FIG. 6 shows the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at the genus level by performing the metagenome analysis after separating bacterial-derived vesicles from the blood of patients with lung cancer and asthma.

The present invention relates to a method for diagnosing lung cancer through the analysis of a bacterial metagenome in an asthmatic patient. The present inventors extracted a gene from a bacterial-derived extracellular vesicle using a sample derived from a subject and conducted a metagenome analysis thereof. The extracellular vesicles derived from bacteria that could act as causative factors were identified.

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

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

(c) comparing the content of the bacterial-derived extracellular vesicles with the sample derived from asthmatic patients through sequence analysis of the PCR product.

The term "diagnosis of lung cancer" as used in the present invention means to determine whether or not lung cancer is likely to develop in a patient, whether the likelihood of lung cancer development is relatively high, or whether lung cancer has already developed. The method of the present invention can be used to slow the onset or prevent the onset of disease through special and appropriate management as a patient at high risk for lung cancer for any particular patient. In addition, the methods of the present invention can be used clinically to determine treatment by early diagnosis of lung cancer and by selecting the most appropriate treatment regimen.

The diagnosis of lung cancer according to the present invention is advantageous in simultaneously estimating various sub-types of lung cancer such as adenocarcinoma, small cell carcinoma, and squamous cell carcinoma.

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

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

In one embodiment of the present invention, metagenomic analysis of extracellular vesicles derived from bacteria in the blood of asthmatic and lung cancer patients was carried out, and phylum, class, order, family, and Were analyzed at the genus level to identify bacterial-derived vesicles that could act as causative agents of lung cancer in asthmatic patients.

More specifically, in one embodiment of the present invention, the bacterial-derived metagenomes were analyzed at the door level. As a result, bacteria derived from Bacteroidetes, Cyanobacteria, TM7, Fusobacteria, Thermi, Verrucomicrobia, Armatimonadetes, Acidobacteria, Gemmatimonadetes, And asthma patients (see Example 4).

More specifically, in one embodiment of the present invention, the bacterial-derived metagenomes were analyzed at the level of a river, and as a result, Bacteroidia, Bacilli, Flavobacterias, Sphingobacterias, Alphaproteobacteria, Fusobacterias, TM7-3, Deinococci, Verrucomicrobiae, Saprospirae, Chloroplast, Cytophagia, Fimbriimonadia , Chloracidobacteria, Thermomicrobia, Thermoleophilia, and Solibacteres were significantly different between lung cancer patients and asthmatic patients (see Example 4).

More specifically, in one embodiment of the present invention, the metagenome derived from a bacterium was analyzed at the neck level. As a result, YS2, Turicibacterales, Bifidobacteriales, Bacteroidales, Enterobacteriales, Rhodobacterales, Gemellales, Flavobacteriales, Caulobacterales, Neisseriales, Sphingobacteriales, Deinococcales, Pseudomonadales, Rhodocyclales , Xanthomonadales, Fusobacteriales, Actinomycetales, Sphingomonadales, Verrucomicrobiales, Saprospirales, Rhizobiales, Bacillales, Streptophyta, Cytophagales, Thermales, Fimbriimonadales, CW040, Rickettsiales, RB41, Alteromonadales, JG30-KF-CM45, I025, Aeromonadales and Solibacterales There was a significant difference between lung cancer patients and asthma patients (see Example 4).

More specifically, in one embodiment of the present invention, the metagenome from the bacterium was analyzed at an over-level, and as a result, it was found that Helicobacteraceae, Bacteriaceae, Turicibacteraceae, , Leptotrichiaceae, Moraxellaceae, Caulobacteraceae, Erythrobacteraceae, Hyphomicrobiaceae, Neisseriaceae, Sphingobacteriaceae, Deinococcaceae, Aerococcaceae, Bartonellaceae, Micrococcaceae, Flavobacteriaceae, Burkholderiaceae, Lactobacillaceae, Dietziaceae, Rhodocyclaceae, Xanthomonadaceae, Geodermatophilaceae, Actinomycetaceae, Methylobacteriaceae, Pseudomonadaceae, Corynebacteriaceae, Staphylococcaceae, Nocardioidaceae, Verrucomicrobiaceae , Sphingomonadaceae, Mycobacteriaceae, Tissierellaceae, Chitinophagaceae, Intrasporangiaceae, Propionibacteriaceae, Aurantimonadaceae, Planococcaceae, Fusobacteriaceae, Bradyrhizobiaceae, Nocardiaceae, Dermabacteraceae, Bacillaceae, Thermaceae, Ellin6075, Brevibacteriaceae, Microbacteriaceae, Rhodospirillaceae, Cytophagaceae, Fimbriimonadaceae, Dermacoccaceae, Chromatiaceae, Rhizobiaceae, Gordoniaceae, mitochondria, Pseudonocardiaceae, Exiguobacteraceae, Shewanellaceae, F16, Rs-045, and Aeromonadaceae and bacteria-derived vesicles lung There was a significant difference between the patient and asthmatic patients (see Example 4).

More specifically, in one embodiment of the present invention, the genome-wide analysis of the germ-derived metagenomes revealed that the genomic region of the genus Bacillus was selected from the group consisting of Trabulsiella, Enterobacter, Veillonella, Bifidobacterium, Lachnospira, Comamonas, Bacteroides, Turicibacter, Sutterella, Klebsiella, SMB53, Roseburia, Dialister, , Parabacteroides, Butyricimonas, Odoribacter, Eubacterium, Dorea, Enhydrobacter, Granulicatella, Chryseobacterium, Porphyromonas, Coprococcus, Peptoniphilus, Microbispora, Deinococcus, Acinetobacter, Aerococcus, Actinomyces, Brevundimonas, Blastomonas, Citrobacter, Lactobacillus, Stenotrophomonas, Corynebacterium, Pseudomonas, Lautropia, Akkermansia , Staphylococcus, Bacillus, Sphingobacterium, Anaerococcus, Neisseria, Leptotrichia, Mycobacterium, Kocuria, Methylobacterium, Propionibacterium, Hymenobacter, Sphingomonas, Fusobacterium, Brachybacterium, Rhodococcus, Micrococcus, Kaistobacter, Finegoldia, Rubellimicrobium, Brevibacterium, Agrobacterium, Dietzia, Fimbriimonas, Flavobacterium, Der There was a significant difference between lung cancer patients and asthma patients (see Example 4). The vesicles derived from bacteria belonging to the genera Macoccus, Skermanella, Novosphingobium, Gordonia, Rheinheimera, Achromobacter, Hydrogenophilus, Thermus, Exiguobacterium, Shewanella, Ralstonia and Alkanindiges.

The present invention is based on the results of the above-mentioned Examples, and the meta genome analysis of the extracellular vesicles derived from the bacterium isolated from the blood was performed to identify bacterial-derived vesicles whose content was significantly changed in lung cancer patients as compared with the asthmatic patients, Through the meta genome analysis, it was confirmed that lung cancer can be diagnosed by analyzing the increase and decrease of bacterial-derived vesicles at the above-mentioned levels.

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

[Example]

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

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

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

Example 2 Separation of Vesicles from Blood and DNA Extraction

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

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

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

Example 3. Metagenomic analysis using DNA extracted from vesicles in blood

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

Example 4 Diagnosis of Lung Cancer Based on Bacillus-Derived Bacterial Meta-genome from Blood of Asthmatic Patients and Lung Cancer Patients

In the method of Example 3, meta genome sequencing was performed after separating vesicles from 308 lung cancer patients and 277 asthmatic patients. For the development of the diagnostic model, first the p value between the two groups was less than 0.05 and the difference between the two groups was more than 2 times, and the logistic regression analysis was used to determine the diagnostic performance index AUC under curve, sensitivity, and specificity.

Analysis of bacterial-derived vesicles in the blood at the phylum level revealed that a diagnostic model was developed with one or more biomarkers from Bacteroidetes, Cyanobacteria, TM7, Fusobacteria, Thermi, Verrucomicrobia, Armatimonadetes, Acidobacteria, Gemmatimonadetes, and Chloroflexi , And lung cancer (Table 2 and Fig. 2).

asthma Lung cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value fold Auc sensitivity specificity Auc sensitivity specificity p__Bacteroidetes 0.1762 0.0499 0.0757 0.0370 0.0000 0.43 0.97 0.88 0.93 0.96 0.90 0.91 p__Cyanobacteria 0.0043 0.0119 0.0189 0.0319 0.0000 4.39 0.89 0.70 0.86 0.85 0.75 0.80 p__TM7 0.0010 0.0029 0.0051 0.0080 0.0000 4.98 0.87 0.65 0.90 0.78 0.69 0.74 p__Fusobacteria 0.0009 0.0029 0.0047 0.0095 0.0000 5.04 0.85 0.63 0.89 0.84 0.68 0.84 p__Thermi 0.0006 0.0019 0.0031 0.0065 0.0000 5.25 0.83 0.62 0.88 0.80 0.65 0.82 p__Verrucomicrobia 0.0053 0.0060 0.0277 0.0236 0.0000 5.25 0.91 0.79 0.82 0.87 0.78 0.77 p__Armatimonadetes 0.0001 0.0004 0.0009 0.0051 0.0046 8.82 0.81 0.62 0.87 0.76 0.65 0.78 p__Acidobacteria 0.0003 0.0011 0.0029 0.0084 0.0000 8.85 0.83 0.62 0.89 0.82 0.63 0.80 p__Gemmatimonadetes 0.0001 0.0002 0.0005 0.0019 0.0000 10.04 0.80 0.62 0.86 0.77 0.66 0.77 p__Chloroflexi 0.0001 0.0003 0.0022 0.0047 0.0000 22.41 0.84 0.62 0.91 0.87 0.63 0.89

As a result of analyzing the bacterial-derived vesicles in the blood at the level of the class, it was found that Bacteroidia, Bacilli, Flavobacterias, Sphingobacterias, Alphaproteobacteria, Fusobacterias, TM7-3, Deinococci, Verrucomicrobiae, Saprospirae, Chloroplast, Cytophagia, Fimbriimonadia, Chloracidobacteria, Thermomicrobia, And Solibacteres river bacterium, the diagnostic performance of lung cancer was significant (see Table 3 and Figure 3).

asthma Lung cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value fold AUC sensitivity specificity AUC sensitivity specificity c__Bacteroidia 0.1736 0.0514 0.0661 0.0380 0.0000 0.38 0.97 0.89 0.94 0.95 0.88 0.94 c__Bacilli 0.0611 0.0408 0.1404 0.0728 0.0000 2.30 0.92 0.81 0.91 0.91 0.85 0.91 c__Flavobacteriia 0.0018 0.0066 0.0055 0.0088 0.0000 3.07 0.86 0.62 0.86 0.80 0.72 0.86 c__Sphingobacteriia 0.0004 0.0011 0.0014 0.0042 0.0000 3.74 0.81 0.62 0.85 0.76 0.66 0.85 c__Alphaproteobacteria 0.0163 0.0323 0.0700 0.0453 0.0000 4.29 0.95 0.82 0.91 0.92 0.85 0.91 c__Fusobacteriia 0.0009 0.0029 0.0047 0.0095 0.0000 5.04 0.85 0.63 0.89 0.84 0.68 0.89 c__TM7-3 0.0009 0.0027 0.0048 0.0079 0.0000 5.19 0.87 0.66 0.90 0.77 0.68 0.90 c__Deinococci 0.0006 0.0019 0.0031 0.0065 0.0000 5.25 0.83 0.62 0.88 0.80 0.65 0.88 c__ Verrucomicrobiae 0.0052 0.0060 0.0274 0.0235 0.0000 5.29 0.91 0.79 0.83 0.87 0.76 0.83 c__Saprospirae 0.0002 0.0006 0.0010 0.0034 0.0000 5.34 0.81 0.63 0.88 0.77 0.63 0.88 c__Chloroplast 0.0027 0.0049 0.0169 0.0315 0.0000 6.22 0.89 0.68 0.87 0.86 0.75 0.87 c__Cytophagia 0.0002 0.0008 0.0014 0.0040 0.0000 6.73 0.82 0.62 0.86 0.77 0.68 0.86 c__Fimbriimonadia 0.0001 0.0004 0.0009 0.0051 0.0046 8.81 0.81 0.62 0.87 0.76 0.65 0.87 c__Chloracidobacteria 0.0001 0.0005 0.0010 0.0052 0.0015 11.88 0.81 0.62 0.90 0.76 0.65 0.90 c__Termomicrobia 0.0000 0.0002 0.0012 0.0033 0.0000 26.04 0.82 0.62 0.89 0.84 0.63 0.89 c__Termoleophilia 0.0000 0.0002 0.0010 0.0064 0.0069 28.53 0.80 0.62 0.87 0.77 0.66 0.87 c__Solibacteres 0.0000 0.0002 0.0013 0.0051 0.0000 41.68 0.81 0.61 0.87 0.82 0.66 0.87

Bacterial-derived parasites in the blood were analyzed at the order level and the results showed that YS2, Turicibacterales, Bifidobacteriales, Bacteroidales, Enterobacteriales, Rhodobacterales, Gemellales, Flavobacteriales, Caulobacterales, Neisseriales, Sphingobacteriales, Deinococcales, Pseudomonadales, Rhodocyclales, Xanthomonadales, Fusobacteriales, Actinomycetales, Diagnostic models were developed with one or more biomarkers from the bacterium Streptococcus, Sphingomonadales, Verrucomicrobiales, Saprospirales, Rhizobiales, Bacillales, Streptophyta, Cytophagales, Thermales, Fimbriimonadales, CW040, Rickettsiales, RB41, Alteromonadales, JG30-KF-CM45, I025, Aeromonadales and Solibacterales , The diagnostic performance for lung cancer was significant (see Table 4 and Figure 4).

asthma Lung cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value fold AUC sensitivity specificity AUC sensitivity specificity o__YS2 0.0007 0.0014 0.0001 0.0009 0.0000 0.16 0.81 0.64 0.86 0.81 0.66 0.75 o__Turicibacterales 0.0017 0.0053 0.0004 0.0017 0.0009 0.25 0.80 0.62 0.86 0.77 0.65 0.74 o__Bifidobacteriales 0.0627 0.0335 0.0164 0.0144 0.0000 0.26 0.95 0.85 0.95 0.92 0.82 0.88 o__Bacteroidales 0.1736 0.0514 0.0661 0.0380 0.0000 0.38 0.97 0.89 0.94 0.95 0.88 0.92 o__Eterobacteriales 0.2091 0.0878 0.0983 0.0629 0.0000 0.47 0.91 0.78 0.91 0.91 0.81 0.85 o__Rhodobacterales 0.0034 0.0237 0.0080 0.0125 0.0092 2.36 0.80 0.62 0.86 0.77 0.68 0.74 o__Gemellales 0.0004 0.0032 0.0012 0.0032 0.0089 2.64 0.79 0.62 0.86 0.76 0.66 0.75 o__Flavobacteriales 0.0018 0.0066 0.0055 0.0088 0.0000 3.07 0.86 0.62 0.86 0.80 0.72 0.82 o__Caulobacterales 0.0015 0.0030 0.0050 0.0072 0.0000 3.31 0.85 0.61 0.89 0.80 0.71 0.77 o__Neisseriales 0.0016 0.0064 0.0058 0.0119 0.0000 3.61 0.82 0.62 0.87 0.80 0.69 0.76 o__Sphingobacteriales 0.0004 0.0011 0.0014 0.0042 0.0000 3.74 0.81 0.62 0.85 0.76 0.66 0.74 o__Deinococcales 0.0004 0.0013 0.0014 0.0053 0.0010 3.89 0.79 0.62 0.86 0.76 0.66 0.75 o__Pseudomonadales 0.0335 0.0496 0.1334 0.0791 0.0000 3.98 0.96 0.87 0.92 0.93 0.87 0.86 o__Rhodocyclales 0.0003 0.0009 0.0012 0.0045 0.0006 4.31 0.80 0.62 0.86 0.75 0.65 0.75 o__Xanthomonadales 0.0006 0.0012 0.0025 0.0059 0.0000 4.47 0.81 0.62 0.88 0.77 0.68 0.76 o__Fusobacteriales 0.0009 0.0029 0.0047 0.0095 0.0000 5.04 0.85 0.63 0.89 0.84 0.68 0.84 o__Actinomycetales 0.0160 0.0218 0.0813 0.0458 0.0000 5.07 0.97 0.90 0.92 0.96 0.88 0.88 o__Sphingomonadales 0.0049 0.0082 0.0261 0.0254 0.0000 5.27 0.91 0.74 0.88 0.91 0.79 0.80 o__Verrucomicrobiales 0.0052 0.0060 0.0274 0.0235 0.0000 5.29 0.91 0.79 0.83 0.87 0.76 0.77 o__Saprospirales 0.0002 0.0006 0.0010 0.0034 0.0000 5.34 0.81 0.63 0.88 0.77 0.63 0.73 o__Rhizobiales 0.0043 0.0057 0.0237 0.0202 0.0000 5.54 0.93 0.80 0.87 0.90 0.84 0.85 o__Bacillales 0.0071 0.0085 0.0394 0.0509 0.0000 5.57 0.94 0.84 0.88 0.92 0.82 0.85 o__Streptophyta 0.0027 0.0048 0.0165 0.0314 0.0000 6.23 0.89 0.68 0.88 0.86 0.75 0.82 o__Cytophagales 0.0002 0.0008 0.0014 0.0040 0.0000 6.73 0.82 0.62 0.86 0.77 0.68 0.78 o__Thermales 0.0002 0.0014 0.0017 0.0039 0.0000 7.32 0.83 0.60 0.89 0.79 0.66 0.79 o__Fimbriimonadales 0.0001 0.0004 0.0009 0.0051 0.0046 8.81 0.81 0.62 0.87 0.76 0.65 0.78 o__CW040 0.0002 0.0009 0.0016 0.0040 0.0000 9.42 0.84 0.62 0.88 0.76 0.66 0.75 o__Rickettsiales 0.0003 0.0011 0.0034 0.0089 0.0000 12.76 0.86 0.61 0.90 0.76 0.62 0.74 o__RB41 0.0001 0.0005 0.0010 0.0052 0.0016 12.88 0.81 0.62 0.89 0.76 0.65 0.76 o__Alteromonadales 0.0002 0.0007 0.0022 0.0081 0.0000 13.64 0.82 0.62 0.86 0.79 0.66 0.78 o__JG30-KF-CM45 0.0000 0.0002 0.0011 0.0033 0.0000 24.15 0.82 0.62 0.89 0.84 0.63 0.84 o__I025 0.0001 0.0005 0.0019 0.0059 0.0000 27.72 0.83 0.62 0.88 0.79 0.66 0.78 o__Aeromonadales 0.0002 0.0007 0.0056 0.0320 0.0030 34.45 0.82 0.62 0.87 0.78 0.68 0.74 o__Solibacterales 0.0000 0.0002 0.0013 0.0051 0.0000 41.68 0.81 0.61 0.87 0.82 0.66 0.82

Bacterial-derived vesicles in the blood were analyzed at the level of the family, and as a result of analysis at the family level, it was found that Helicobacteraceae, Bacteroidaceae, Turicibacteraceae, Veillonellaceae, Bifidobacteriaceae, Barnesiellaceae, Rikenellaceae, Clostridiaceae, Odoribacteraceae, Enterobacteriaceae, Porphyromonadaceae, Gemellaceae, Weeksellaceae, Carnobacteriaceae, Leptotrichiaceae, Erythrobacteraceae, Hyphomicrobiaceae, Neisseriaceae, Sphingobacteriaceae, Deinococcaceae, Aerococcaceae, Bartonellaceae, Micrococcaceae, Flavobacteriaceae, Burkholderiaceae, Lactobacillaceae, Dietziaceae, Rhodocyclaceae, Xanthomonadaceae, Geodermatophilaceae, Actinomycetaceae, Methylobacteriaceae, Pseudomonadaceae, Corynebacteriaceae, Staphylococcaceae, Nocardioidaceae, Verrucomicrobiaceae, Sphingomonadaceae, Mycobacteriaceae, Tissierellaceae, Chitinophagaceae, Intrasporangiaceae, Propionibacteriaceae, Aurantimonadaceae, Planococcaceae, Fusobacteriaceae, Bradyrhizobiaceae, Nocardiaceae, Dermabacteraceae, Bacillaceae, Th Development of a diagnostic model with one or more biomarkers from ermaceae, Ellin6075, Brevibacteriaceae, Microbacteriaceae, Rhodospirillaceae, Cytophagaceae, Fimbriimonadaceae, Dermacoccaceae, Chromatiaceae, Rhizobiaceae, Gordoniaceae, mitochondria, Pseudonocardiaceae, Exiguobacteraceae, Shewanellaceae, F16, Rs-045 and Aeromonadaceae and bacteria , The diagnostic performance for lung cancer was significant (see Table 5 and Figure 5).

asthma Lung cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value fold AUC sensitivity specificity AUC sensitivity specificity f__Helicobacteraceae 0.0005 0.0022 0.0000 0.0002 0.0024 0.05 0.79 0.64 0.86 0.77 0.68 0.75 f__Bacteroidaceae 0.1140 0.0469 0.0276 0.0212 0.0000 0.24 0.97 0.89 0.95 0.96 0.91 0.91 f__Turicibacteraceae 0.0017 0.0053 0.0004 0.0017 0.0009 0.25 0.80 0.62 0.86 0.77 0.65 0.74 f__Veillonellaceae 0.0375 0.0288 0.0094 0.0093 0.0000 0.25 0.94 0.79 0.91 0.93 0.82 0.88 f__Bifidobacteriaceae 0.0627 0.0335 0.0164 0.0144 0.0000 0.26 0.95 0.85 0.95 0.92 0.82 0.88 f__Barnesiellaceae 0.0024 0.0050 0.0008 0.0025 0.0000 0.32 0.80 0.64 0.85 0.79 0.65 0.77 f__Rikenellaceae 0.0070 0.0087 0.0025 0.0050 0.0000 0.36 0.83 0.68 0.84 0.81 0.68 0.79 f__Clostridiaceae 0.0151 0.0143 0.0059 0.0078 0.0000 0.39 0.82 0.66 0.86 0.81 0.69 0.82 f__Odoribacteraceae 0.0021 0.0029 0.0009 0.0022 0.0000 0.43 0.80 0.67 0.86 0.78 0.65 0.75 f__Eterobacteriaceae 0.2091 0.0878 0.0983 0.0629 0.0000 0.47 0.91 0.78 0.91 0.91 0.81 0.85 f__Porphyromonadaceae 0.0145 0.0090 0.0069 0.0105 0.0000 0.47 0.85 0.68 0.85 0.83 0.72 0.80 f__Gemellaceae 0.0004 0.0032 0.0012 0.0032 0.0093 2.65 0.79 0.62 0.86 0.76 0.66 0.75 f__Weeksellaceae 0.0013 0.0061 0.0036 0.0072 0.0001 2.68 0.82 0.61 0.89 0.78 0.66 0.80 f__Carnobacteriaceae 0.0003 0.0012 0.0010 0.0029 0.0002 3.14 0.79 0.62 0.86 0.76 0.66 0.77 f__Leptotrichiaceae 0.0004 0.0011 0.0012 0.0029 0.0000 3.23 0.81 0.62 0.89 0.77 0.62 0.79 f__Moraxellaceae 0.0175 0.0451 0.0567 0.0481 0.0000 3.25 0.90 0.68 0.89 0.86 0.74 0.82 f__Caulobacteraceae 0.0015 0.0030 0.0050 0.0072 0.0000 3.31 0.85 0.61 0.89 0.80 0.71 0.77 f__Erythrobacteraceae 0.0003 0.0028 0.0010 0.0045 0.0235 3.36 0.79 0.62 0.86 0.75 0.66 0.74 f__Hyphomicrobiaceae 0.0001 0.0006 0.0004 0.0015 0.0020 3.36 0.79 0.62 0.87 0.76 0.65 0.74 f__Neisseriaceae 0.0016 0.0064 0.0058 0.0119 0.0000 3.61 0.82 0.62 0.87 0.80 0.69 0.76 f__Sphingobacteriaceae 0.0003 0.0010 0.0013 0.0041 0.0000 3.91 0.80 0.61 0.86 0.76 0.65 0.74 f__Deinococcaceae 0.0003 0.0013 0.0014 0.0053 0.0012 4.00 0.79 0.62 0.86 0.76 0.66 0.75 f__Aerococcaceae 0.0017 0.0042 0.0067 0.0107 0.0000 4.02 0.84 0.63 0.87 0.83 0.72 0.79 f__Bartonellaceae 0.0002 0.0007 0.0009 0.0033 0.0004 4.06 0.80 0.62 0.87 0.76 0.65 0.74 f__Micrococcaceae 0.0045 0.0143 0.0183 0.0172 0.0000 4.07 0.91 0.65 0.89 0.87 0.76 0.79 f__Flavobacteriaceae 0.0005 0.0022 0.0019 0.0049 0.0000 4.15 0.83 0.61 0.87 0.78 0.68 0.77 f__Burkholderiaceae 0.0004 0.0010 0.0017 0.0064 0.0005 4.16 0.79 0.62 0.86 0.77 0.66 0.76 f__Lactobacillaceae 0.0108 0.0141 0.0454 0.0454 0.0000 4.21 0.91 0.80 0.86 0.89 0.85 0.82 f__Dietziaceae 0.0003 0.0008 0.0011 0.0049 0.0031 4.25 0.79 0.62 0.87 0.76 0.66 0.76 f__Rhodocyclaceae 0.0003 0.0009 0.0012 0.0045 0.0006 4.31 0.80 0.62 0.86 0.75 0.65 0.75 f__Xanthomonadaceae 0.0005 0.0012 0.0023 0.0058 0.0000 4.36 0.81 0.62 0.88 0.77 0.66 0.76 f__Geodermatophilaceae 0.0003 0.0012 0.0012 0.0034 0.0000 4.44 0.80 0.61 0.86 0.79 0.65 0.77 f__Actinomycetaceae 0.0008 0.0038 0.0035 0.0060 0.0000 4.46 0.83 0.62 0.89 0.82 0.65 0.79 f__Methylobacteriaceae 0.0015 0.0034 0.0066 0.0083 0.0000 4.49 0.86 0.64 0.86 0.81 0.69 0.74 f__Pseudomonadaceae 0.0160 0.0154 0.0765 0.0531 0.0000 4.77 0.97 0.91 0.95 0.95 0.93 0.88 f__Corynebacteriaceae 0.0048 0.0055 0.0238 0.0241 0.0000 4.95 0.91 0.79 0.85 0.90 0.84 0.80 f__Staphylococcaceae 0.0047 0.0068 0.0242 0.0437 0.0000 5.15 0.90 0.73 0.87 0.90 0.75 0.84 f__Nocardioidaceae 0.0003 0.0008 0.0017 0.0046 0.0000 5.16 0.81 0.61 0.85 0.77 0.68 0.74 f__Verrucomicrobiaceae 0.0052 0.0060 0.0274 0.0235 0.0000 5.29 0.91 0.79 0.83 0.87 0.76 0.77 f__Sphingomonadaceae 0.0046 0.0070 0.0249 0.0245 0.0000 5.39 0.91 0.74 0.87 0.91 0.81 0.82 f__Mycobacteriaceae 0.0002 0.0009 0.0012 0.0054 0.0023 5.60 0.79 0.62 0.87 0.77 0.63 0.76 f__Tissierellaceae 0.0006 0.0016 0.0033 0.0069 0.0000 5.62 0.83 0.60 0.88 0.80 0.65 0.82 f__Chitinophagaceae 0.0002 0.0006 0.0010 0.0034 0.0000 5.63 0.81 0.63 0.88 0.76 0.63 0.73 f__Intrasporangiaceae 0.0006 0.0013 0.0034 0.0065 0.0000 5.74 0.84 0.62 0.86 0.80 0.71 0.76 f__Propionibacteriaceae 0.0021 0.0031 0.0126 0.0117 0.0000 5.85 0.91 0.75 0.87 0.93 0.79 0.86 f__Aurantimonadaceae 0.0001 0.0003 0.0005 0.0020 0.0016 6.06 0.79 0.62 0.86 0.76 0.66 0.74 f__Planococcaceae 0.0007 0.0013 0.0045 0.0108 0.0000 6.20 0.87 0.64 0.85 0.83 0.66 0.83 f__Fusobacteriaceae 0.0006 0.0026 0.0035 0.0090 0.0000 6.21 0.83 0.62 0.88 0.83 0.68 0.85 f__Bradyrhizobiaceae 0.0003 0.0008 0.0020 0.0054 0.0000 6.25 0.82 0.60 0.86 0.77 0.65 0.77 f__Nocardiaceae 0.0008 0.0019 0.0048 0.0103 0.0000 6.32 0.86 0.65 0.87 0.82 0.74 0.82 f__Dermabacteraceae 0.0002 0.0005 0.0010 0.0030 0.0000 6.38 0.80 0.61 0.88 0.77 0.65 0.76 f__Bacillaceae 0.0013 0.0020 0.0085 0.0149 0.0000 6.44 0.88 0.64 0.88 0.82 0.72 0.75 f__Thermaceae 0.0002 0.0014 0.0017 0.0039 0.0000 7.32 0.83 0.60 0.89 0.79 0.66 0.79 f__Ellin6075 0.0001 0.0004 0.0005 0.0018 0.0001 7.54 0.80 0.62 0.89 0.76 0.65 0.77 f__Brevibacteriaceae 0.0002 0.0006 0.0015 0.0061 0.0002 7.65 0.81 0.62 0.89 0.77 0.66 0.76 f__Microbacteriaceae 0.0002 0.0008 0.0013 0.0038 0.0000 7.69 0.82 0.62 0.86 0.79 0.68 0.77 f__Rhodospirillaceae 0.0002 0.0011 0.0015 0.0062 0.0002 7.94 0.79 0.62 0.87 0.77 0.66 0.76 f__Cytophagaceae 0.0002 0.0005 0.0013 0.0040 0.0000 8.57 0.82 0.61 0.87 0.77 0.63 0.78 f__Fimbriimonadaceae 0.0001 0.0004 0.0009 0.0051 0.0048 8.78 0.81 0.62 0.87 0.76 0.65 0.78 f__Dermacoccaceae 0.0002 0.0005 0.0019 0.0048 0.0000 9.05 0.83 0.63 0.88 0.80 0.69 0.78 f__Chromatiaceae 0.0001 0.0006 0.0011 0.0062 0.0056 9.57 0.80 0.62 0.87 0.75 0.66 0.74 f__Rhizobiaceae 0.0012 0.0025 0.0118 0.0150 0.0000 9.97 0.92 0.76 0.88 0.90 0.76 0.83 f__Gordoniaceae 0.0001 0.0005 0.0013 0.0048 0.0000 10.20 0.80 0.62 0.85 0.77 0.65 0.76 f__mitochondria 0.0003 0.0011 0.0027 0.0081 0.0000 10.41 0.84 0.60 0.89 0.75 0.60 0.74 f__Pseudonocardiaceae 0.0001 0.0002 0.0006 0.0025 0.0001 11.72 0.80 0.63 0.86 0.76 0.63 0.75 f__Exiguobacteraceae 0.0000 0.0002 0.0006 0.0033 0.0048 23.39 0.80 0.62 0.86 0.77 0.65 0.79 f__Shewanellaceae 0.0000 0.0004 0.0009 0.0040 0.0003 24.54 0.80 0.62 0.86 0.77 0.66 0.77 f__F16 0.0001 0.0005 0.0016 0.0040 0.0000 24.68 0.85 0.63 0.88 0.78 0.68 0.74 f__Rs-045 0.0001 0.0005 0.0017 0.0058 0.0000 26.38 0.83 0.62 0.90 0.78 0.65 0.78 f__Aeromonadaceae 0.0002 0.0007 0.0056 0.0320 0.0030 34.42 0.82 0.62 0.87 0.78 0.68 0.74

Bacterial-derived vesicles in the blood were analyzed at the genus level and found to be in the order of the genus, Trabulsiella, Enterobacter, Veillonella, Bifidobacterium, Lachnospira, Comamonas, Bacteroides, Turicibacter, Sutterella, Klebsiella, SMB53, Roseburia, Dialister, Ruminococcus, Parabacteroides, Butyricimonas, Odoribacter, Eubacterium, Dorea, Enhydrobacter, Granulicatella, Chryseobacterium, Porphyromonas, Coprococcus, Peptoniphilus, Microbispora, Deinococcus, Acinetobacter, Aerococcus, Actinomyces, Brevundimonas, Blastomonas, Citrobacter, Lactobacillus, Stenotrophomonas, Corynebacterium, Pseudomonas, Lautropia, Akkermansia, Staphylococcus, Bacillus, Sphingobacterium, Anaerococcus, Neisseria, Leptotrichia, Mycobacterium, Kocuria, Methylobacterium, Propionibacterium, Hymenobacter, Sphingomonas, Fusobacterium, Brachybacterium, Rhodococcus, Micrococcus, Kaistobacter, Finegoldia, Rubellimicrobium, Brevibacterium, Agrobacterium, Dietzia, Fimbriimonas, Flavobacterium, Dermacoccus, Skermanella, Novosphingobium, Gordonia, When diagnostic models were developed with one or more biomarkers from Rheinheimera, Achromobacter, Hydrogenophilus, Thermus, Exiguobacterium, Shewanella, Ralstonia, and Alkanindiges bacteria, diagnostic performance for lung cancer was significant (see Table 6 and FIG. 6).

asthma Lung cancer t-test Training Set Test Set Taxon Mean SD Mean SD p-value fold AUC sensitivity specificity AUC sensitivity specificity g__Trabulsiella 0.0007 0.0012 0.0000 0.0002 0.0000 0.04 0.92 0.75 0.92 0.91 0.82 0.89 g__Eterobacter 0.0016 0.0013 0.0002 0.0018 0.0000 0.11 0.93 0.77 0.93 0.91 0.79 0.87 g__Veillonella 0.0243 0.0248 0.0035 0.0054 0.0000 0.14 0.94 0.82 0.91 0.93 0.84 0.87 g__Bifidobacterium 0.0623 0.0336 0.0123 0.0120 0.0000 0.20 0.96 0.87 0.96 0.95 0.82 0.92 g__Lachnospira 0.0037 0.0130 0.0008 0.0022 0.0013 0.23 0.86 0.67 0.87 0.82 0.72 0.78 g__Comamonas 0.0031 0.0091 0.0007 0.0023 0.0002 0.23 0.81 0.63 0.86 0.80 0.69 0.78 g__Bacteroides 0.1140 0.0469 0.0276 0.0212 0.0000 0.24 0.97 0.89 0.95 0.96 0.91 0.91 g__Turicibacter 0.0017 0.0053 0.0004 0.0017 0.0009 0.25 0.80 0.62 0.86 0.77 0.65 0.74 g__Sutterella 0.0014 0.0029 0.0004 0.0013 0.0000 0.27 0.80 0.66 0.84 0.77 0.62 0.77 g__Klebsiella 0.0018 0.0012 0.0006 0.0020 0.0000 0.31 0.90 0.72 0.87 0.88 0.79 0.79 g__SMB53 0.0023 0.0033 0.0007 0.0015 0.0000 0.32 0.81 0.63 0.86 0.78 0.69 0.74 g__Roseburia 0.0021 0.0029 0.0007 0.0031 0.0000 0.33 0.80 0.64 0.86 0.78 0.65 0.76 g__Dialister 0.0090 0.0077 0.0033 0.0047 0.0000 0.37 0.86 0.67 0.87 0.86 0.76 0.78 g__Ruminococcus 0.0163 0.0112 0.0062 0.0102 0.0000 0.38 0.89 0.72 0.88 0.84 0.76 0.78 g__Parabacteroides 0.0138 0.0086 0.0055 0.0099 0.0000 0.40 0.87 0.70 0.87 0.86 0.75 0.86 g__Butyricimonas 0.0012 0.0019 0.0005 0.0016 0.0000 0.42 0.80 0.64 0.85 0.78 0.63 0.74 g__Odoribacter 0.0009 0.0017 0.0004 0.0015 0.0008 0.46 0.80 0.64 0.87 0.76 0.65 0.76 g__Eubacterium 0.0007 0.0013 0.0015 0.0028 0.0000 2.09 0.81 0.61 0.87 0.76 0.63 0.76 g__Dorea 0.0017 0.0072 0.0037 0.0061 0.0009 2.18 0.82 0.61 0.87 0.79 0.69 0.78 g__Enhydrobacter 0.0093 0.0425 0.0236 0.0293 0.0000 2.54 0.84 0.62 0.86 0.78 0.68 0.75 g__Granulicatella 0.0003 0.0011 0.0007 0.0024 0.0051 2.58 0.79 0.62 0.86 0.75 0.66 0.76 g__Chryseobacterium 0.0007 0.0025 0.0019 0.0042 0.0000 2.74 0.80 0.62 0.88 0.77 0.66 0.76 g__Porphyromonas 0.0005 0.0036 0.0013 0.0033 0.0065 2.78 0.79 0.62 0.86 0.76 0.66 0.75 g__Coprococcus 0.0047 0.0050 0.0141 0.0146 0.0000 2.97 0.85 0.64 0.89 0.82 0.71 0.79 g__Peptoniphilus 0.0001 0.0005 0.0004 0.0013 0.0012 3.17 0.79 0.61 0.86 0.76 0.66 0.76 g__Microbispora 0.0002 0.0006 0.0006 0.0024 0.0042 3.57 0.79 0.62 0.86 0.75 0.65 0.75 g__Deinococcus 0.0003 0.0013 0.0013 0.0052 0.0020 3.81 0.79 0.62 0.86 0.76 0.66 0.75 g__Acinetobacter 0.0076 0.0128 0.0310 0.0278 0.0000 4.09 0.91 0.78 0.84 0.89 0.78 0.85 g__Aerococcus 0.0005 0.0020 0.0022 0.0075 0.0002 4.10 0.79 0.62 0.86 0.77 0.66 0.77 g__Actinomyces 0.0008 0.0038 0.0032 0.0057 0.0000 4.12 0.82 0.62 0.89 0.81 0.65 0.78 g__Brevundimonas 0.0003 0.0008 0.0012 0.0034 0.0000 4.24 0.80 0.61 0.87 0.78 0.66 0.76 g__Blastomonas 0.0002 0.0016 0.0007 0.0024 0.0022 4.33 0.80 0.62 0.86 0.77 0.66 0.76 g__Citrobacter 0.0050 0.0041 0.0218 0.0220 0.0000 4.34 0.87 0.65 0.87 0.85 0.75 0.79 g__Lactobacillus 0.0101 0.0140 0.0444 0.0454 0.0000 4.39 0.91 0.79 0.85 0.89 0.85 0.79 g__Stenotrophomonas 0.0001 0.0005 0.0005 0.0019 0.0005 4.58 0.79 0.62 0.86 0.75 0.65 0.77 g__Corynebacterium 0.0048 0.0055 0.0238 0.0241 0.0000 4.95 0.91 0.79 0.85 0.90 0.84 0.80 g__Pseudomonas 0.0144 0.0145 0.0734 0.0506 0.0000 5.11 0.97 0.91 0.95 0.96 0.93 0.88 g__Lautropia 0.0002 0.0005 0.0013 0.0060 0.0027 5.15 0.79 0.62 0.86 0.77 0.66 0.75 g__Akkermansia 0.0052 0.0060 0.0273 0.0236 0.0000 5.27 0.91 0.78 0.82 0.87 0.78 0.77 g__Staphylococcus 0.0044 0.0062 0.0233 0.0435 0.0000 5.29 0.90 0.72 0.89 0.90 0.76 0.83 g__Bacillus 0.0008 0.0016 0.0042 0.0108 0.0000 5.34 0.84 0.62 0.89 0.78 0.66 0.80 g__Sphingobacterium 0.0001 0.0006 0.0005 0.0019 0.0011 5.39 0.79 0.61 0.86 0.76 0.66 0.75 g__Anaerococcus 0.0003 0.0013 0.0014 0.0041 0.0000 5.39 0.80 0.62 0.86 0.77 0.63 0.78 g__Neisseria 0.0008 0.0026 0.0041 0.0102 0.0000 5.46 0.83 0.62 0.87 0.80 0.68 0.78 g__Leptotrichia 0.0001 0.0005 0.0006 0.0021 0.0000 5.54 0.80 0.62 0.87 0.76 0.65 0.78 g__Mycobacterium 0.0002 0.0009 0.0012 0.0054 0.0023 5.60 0.79 0.62 0.87 0.77 0.63 0.76 g__Kocuria 0.0004 0.0014 0.0025 0.0058 0.0000 5.66 0.83 0.62 0.88 0.80 0.68 0.82 g__Methylobacterium 0.0007 0.0020 0.0040 0.0068 0.0000 5.70 0.84 0.62 0.86 0.79 0.69 0.78 g__Propionibacterium 0.0021 0.0031 0.0125 0.0117 0.0000 5.84 0.91 0.75 0.87 0.93 0.79 0.85 g__Hymenobacter 0.0001 0.0004 0.0006 0.0021 0.0001 5.97 0.80 0.60 0.86 0.75 0.66 0.75 g__Sphingomonas 0.0025 0.0040 0.0151 0.0166 0.0000 5.99 0.90 0.68 0.90 0.89 0.81 0.78 g__Fusobacterium 0.0006 0.0026 0.0035 0.0090 0.0000 6.21 0.83 0.62 0.88 0.83 0.68 0.85 g__Brachybacterium 0.0001 0.0005 0.0009 0.0030 0.0000 6.22 0.80 0.62 0.87 0.76 0.66 0.75 g__Rhodococcus 0.0008 0.0019 0.0047 0.0102 0.0000 6.25 0.86 0.65 0.87 0.81 0.74 0.80 g__Micrococcus 0.0010 0.0017 0.0063 0.0098 0.0000 6.46 0.86 0.64 0.88 0.81 0.74 0.74 g__Kaistobacter 0.0002 0.0010 0.0016 0.0055 0.0000 6.46 0.81 0.61 0.88 0.76 0.65 0.74 g__Finegoldia 0.0001 0.0006 0.0008 0.0035 0.0003 7.22 0.80 0.62 0.87 0.77 0.65 0.76 g__Rubellimicrobium 0.0001 0.0007 0.0007 0.0040 0.0074 7.31 0.79 0.62 0.86 0.76 0.66 0.74 g__Brevibacterium 0.0002 0.0006 0.0015 0.0061 0.0002 7.65 0.81 0.62 0.89 0.77 0.66 0.76 g__Agrobacterium 0.0002 0.0007 0.0015 0.0036 0.0000 7.73 0.82 0.63 0.88 0.79 0.65 0.78 g__Dietzia 0.0001 0.0006 0.0010 0.0048 0.0017 8.13 0.80 0.63 0.87 0.76 0.66 0.76 g__Fimbriimonas 0.0001 0.0004 0.0009 0.0050 0.0060 8.88 0.81 0.62 0.87 0.76 0.65 0.78 g__Flavobacterium 0.0001 0.0005 0.0012 0.0040 0.0000 8.98 0.82 0.61 0.86 0.77 0.68 0.76 g__Dermacoccus 0.0002 0.0005 0.0019 0.0048 0.0000 9.05 0.83 0.63 0.88 0.80 0.69 0.78 g__Skermanella 0.0001 0.0009 0.0012 0.0058 0.0020 9.54 0.79 0.62 0.87 0.77 0.66 0.76 g__novosphingobium 0.0002 0.0009 0.0019 0.0043 0.0000 9.63 0.83 0.62 0.86 0.80 0.66 0.79 g__Gordonia 0.0001 0.0005 0.0013 0.0048 0.0000 10.20 0.80 0.62 0.85 0.77 0.65 0.76 g__Rheinheimera 0.0001 0.0005 0.0010 0.0062 0.0075 11.51 0.79 0.63 0.87 0.76 0.66 0.74 g__Achromobacter 0.0001 0.0004 0.0006 0.0028 0.0003 12.39 0.81 0.62 0.88 0.75 0.63 0.76 g__Hydrogenophilus 0.0001 0.0007 0.0009 0.0041 0.0011 12.54 0.79 0.62 0.86 0.76 0.66 0.75 g__Thermus 0.0001 0.0007 0.0017 0.0038 0.0000 16.82 0.83 0.61 0.88 0.80 0.66 0.79 g__Exiguobacterium 0.0000 0.0002 0.0006 0.0033 0.0049 23.75 0.80 0.62 0.86 0.77 0.65 0.79 g__Shewanella 0.0000 0.0004 0.0009 0.0040 0.0003 24.61 0.80 0.62 0.86 0.77 0.66 0.77 g__Ralstonia 0.0000 0.0001 0.0010 0.0033 0.0000 28.31 0.82 0.62 0.88 0.80 0.66 0.79 g__Alkanindiges 0.0000 0.0001 0.0005 0.0034 0.0095 40.34 0.80 0.62 0.86 0.74 0.66 0.74

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

<110> MD Healthcare Inc. <120> Method for diagnosis of lung cancer in asthma patients using          analysis of bacteria metagenome <130> MP18-022 <150> KR 10-2017-0083504 <151> 2017-06-30 <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 (5)

A method for providing information for lung cancer diagnosis, comprising the steps of:
(a) extracting DNA from extracellular vesicles isolated from asthmatic patients and subjects;
(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And
(c) comparing the increase or decrease in the content of the bacterial-derived extracellular vesicles with the sample derived from the asthmatic patients through sequence analysis of the PCR product.
The method according to claim 1,
The asthmatic patient and the subject sample are blood,
In step (c), it is preferable to use a bacterial strain such as Bacteroidetes, Cyanobacteria, Fusobacteria, Thermi, Verrucomicrobia, Armatimonadetes, One or more phylum bacterial-derived extracellular vesicles selected from the group consisting of Acidobacteria, Gemmatimonadetes, and Chloroflexi,
Such as Bacteroidia, Bacilli, Flavobacteriia, Sphingobacterias, Alphaproteobacteria, Fusobacterias, Deinococci, But are not limited to, bacteria such as Verrucomicrobiae, Saprospirae, Chloroplast, Cytophagia, Fimbriimonadia, Chloracidobacteria, Thermomicrobia, Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Thermoleophilia and Solibacteres,
But are not limited to, bacteria such as Turicibacterales, Bifidobacteriales, Bacteroidales, Enterobacteriales, Rhodobacterales, Gemellales, Flavobacterium, For example, Flavobacteriales, Caulobacterales, Neisseriales, Sphingobacteriales, Deinococcales, Pseudomonadales, Rhodocyclines ( Such as Rhodocyclales, Xanthomonadales, Fusobacteriales, Actinomycetales, Sphingomonadales, Verrucomicrobiales, Saprospirales, ), Rhizobiales, Bacillales, Streptophyta, Cytophagales, Thermales, Fimbri &lt; (R) &gt; at least one order bacterium selected from the group consisting of iminadales, Rickettsiales, Alteromonadales, Aeromonadales, and Solibacterales. Derived &lt; / RTI &gt; vesicles,
Such as Helicobacteraceae, Bacteroidaceae, Turicibacteraceae, Veillonellaceae, Bifidobacteriaceae, Barnées lacrosis, Bacillus thuringiensis, But are not limited to, Barnesiellaceae, Rikenellaceae, Clostridiaceae, Odoribacteraceae, Enterobacteriaceae, Porphyromonadaceae, Gemellaceae, Weeksellaceae, Carnobacteriaceae, Leptotrichiaceae, Moraxellaceae, Caulobacteraceae, Erythobacteriaceae, For example, Erythrobacteraceae, Hyphomicrobiaceae, Neisseriaceae, Sphingobacteriaceae, Deinococcaceae, Aerococcaceae, Bato Nelashi But are not limited to, bacteria such as Bartonellaceae, Micrococcaceae, Flavobacteriaceae, Burkholderiaceae, Lactobacillaceae, Dietziaceae, For example, Rhodocyclaceae, Xanthomonadaceae, Geodermatophilaceae, Actinomycetaceae, Methylobacteriaceae, Pseudomonadaceae, For example, Corynebacteriaceae, Staphylococcaceae, Nocardioidaceae, Verrucomicrobiaceae, Sphingomonadaceae, Mycobacteriaceae, But are not limited to, Mycobacteriaceae, Tissierellaceae, Chitinophagaceae, Intrasporangiaceae, Propionibacteriaceae, (Aurantimonadaceae), Planococcaceae, Fusobacteriaceae, Bradyrhizobiaceae, Nocardiaceae, Dermabacteraceae, and the like. , Bacillaceae, Thermaceae, Brevibacteriaceae, Microbacteriaceae, Rhodospirillaceae, Cytophagaceae, Pimellae, It has been reported that it has been shown to be effective in the treatment of various diseases such as Fimbriimonadaceae, Dermacoccaceae, Chromatiaceae, Rhizobiaceae, Gordoniaceae, mitochondria, And at least one family derived bacterial cell selected from the group consisting of Pseudonocardiaceae, Exiguobacteraceae, Shewanellaceae, and Aeromonadaceae. Parcel, or
But are not limited to, Trabulsiella, Enterobacter, Veillonella, Bifidobacterium, Lachnospira, Comamonas, Bacteroides, Such as Turicibacter, Sutterella, Klebsiella, Roseburia, Dialister, Ruminococcus, Parabacteroides, Butyricimonas, Odoribacter, Eubacterium, Dorea, Enhydrobacter, Granulicatella, Chryseobacterium, Porphyromonas, Porphyromonas, ), Coprococcus, Peptoniphilus, Microbispora, Deinococcus, Acinetobacter, Aerococcus, Actinomyces (Actinomyces) ), Brevundimon as described above, Blastomonas, Citrobacter, Lactobacillus, Stenotrophomonas, Corynebacterium, Pseudomonas, Lautropia, ), Akkermansia, Staphylococcus, Bacillus, Sphingobacterium, Anaerococcus, Neisseria, Leptotrichia, Myco But are not limited to, Mycobacteria, Mycobacterium, Kocuria, Methylobacterium, Propionibacterium, Hymenobacter, Sphingomonas, Fusobacterium, Such as Brachybacterium, Rhodococcus, Micrococcus, Kaistobacter, Finegoldia, Rubellimicrobium, Brevibacterium, Agrobacterium, foil But are not limited to, Agrobacterium, Dietzia, Fimbriimonas, Flavobacterium, Dermacoccus, Skermanella, Novosphingobium, Gordonia, Rheinheimera, Achromobacter, Hydrogenophilus, Thermus, Exiguobacterium, Shewanella, Ralstonia, ), And Alkanindiges. The method for providing information for diagnosis of lung cancer is characterized by comparing the increase or decrease in the content of one or more genus bacterial-derived extracellular vesicles selected from the group consisting of Alkanindiges.
3. The method of claim 2,
In the step (c), as compared with the sample derived from an asthmatic patient,
But are not limited to, Cyanobacteria, Fusobacteria, Thermi, Verrucomicrobia, Armatimonadetes, Acidobacteria, Gemmatimonadetes, , And chlorophyll (Chloroflexi), which are used in the present invention,
For example, Bacilli, Flavobacteriia, Sphingobacteriia, Alphaproteobacteria, Fusobacterias, Deinococci, Verrucomicrobiae, But are not limited to, Saprospirae, Chloroplast, Cytophagia, Fimbriimonadia, Chloracidobacteria, Thermomicrobia, Thermoleophilia, And Solibacteres, at least one class of bacterial-derived extracellular vesicles,
But are not limited to, Rhodobacterales, Gemellales, Flavobacteriales, Caulobacterales, Neisseriales, Sphingobacteriales, And the like, such as Deinococcales, Pseudomonadales, Rhodocyclales, Xanthomonadales, Fusobacteriales, Actinomycetales, Sphingomonadales Sphingomonadales, Verrucomicrobiales, Saprospirales, Rhizobiales, Bacillales, Streptophyta, Cytophagales, Thermales (including, for example, Thermales, Fimbriimonadales, Rickettsiales, Alteromonadales, Aeromonadales, and Solibacterales as well. At least one neck (order) is selected from the group true luer germ cells derived from outside the package,
Such as Gemellaceae, Weeksellaceae, Carnobacteriaceae, Leptotrichiaceae, Moraxellaceae, Caulobacteraceae, Erythobacteria, Neomycetes, such as Erythrobacteraceae, Hyphomicrobiaceae, Neisseriaceae, Sphingobacteriaceae, Deinococcaceae, Aerococcaceae, , Bartonellaceae, Micrococcaceae, Flavobacteriaceae, Burkholderiaceae, Lactobacillaceae, Dietziaceae, and the like. Rhodocyclaceae, Xanthomonadaceae, Geodermatophilaceae, Actinomycetaceae, Methylobacteriaceae, Pseudomonas aeruginosa, Pseudomonas aeruginosa, Pseudomonadaceae, Corynebacteriaceae, Staphylococcaceae, Nocardioidaceae, Verrucomicrobiaceae, Sphingomonadaceae, Pseudomonadaceae, Corynebacteriaceae, , Mycobacteriaceae, Tissierellaceae, Chitinophagaceae, Intrasporangiaceae, Propionibacteriaceae, Aurantimonaceae, For example, Aurantimonadaceae, Planococcaceae, Fusobacteriaceae, Bradyrhizobiaceae, Nocardiaceae, Dermabacteraceae, (Bacillaceae), Thermaceae, Brevibacteriaceae, Microbacteriaceae, Rhodospirillaceae, Cytophagaceae, Bacillus spp. It has been reported that it has been shown to be effective in the treatment of various diseases such as Fimbriimonadaceae, Dermacoccaceae, Chromatiaceae, Rhizobiaceae, Gordoniaceae, mitochondria, And at least one family derived bacterial cell selected from the group consisting of Pseudonocardiaceae, Exiguobacteraceae, Shewanellaceae, and Aeromonadaceae. Parcel, or
Euroacterium, Dorea, Enhydrobacter, Granulicatella, Chryseobacterium, Porphyromonas, Coprococcus, But are not limited to, Peptoniphilus, Microbispora, Deinococcus, Acinetobacter, Aerococcus, Actinomyces, Brevundimonas, But are not limited to, Blastomonas, Citrobacter, Lactobacillus, Stenotrophomonas, Corynebacterium, Pseudomonas, Lautropia, The present invention relates to the use of the compounds of the formula (I) for the preparation of a medicament for the treatment of a disease or condition selected from the group consisting of Akkermansia, Staphylococcus, Bacillus, Sphingobacterium, Anaerococcus, Neisseria, Leptotrichia, Mycobacterium ), Kocuria, Methylobacterium, Propionibacterium, Hymenobacter, Sphingomonas, Fusobacterium, Brkibacterium, Brachybacterium, Rhodococcus, Micrococcus, Kaistobacter, Finegoldia, Rubellimicrobium, Brevibacterium, Agrobacterium, Agrobacterium, Fimbriimonas, Flavobacterium, Dermacoccus, Skermanella, Novosphingobium, Gordonia, Pseudomonas aeruginosa, Dietzia, Fimbriimonas, Flavobacterium, Rheinheimera, Achromobacter, Hydrogenophilus, Thermus, Exiguobacterium, Shewanella, Ralstonia, and the like. Alkan indiges) is diagnosed as lung cancer when the content of at least one genus bacterium-derived extracellular vesicle is increased.
3. The method of claim 2,
In the step (c), as compared with the sample derived from an asthmatic patient,
Bacteroidetes (phylum) Bacterial-derived extracellular vesicles,
Bacteroidia class Bacterial-derived extracellular vesicles,
From at least one order bacterial-derived extracellular vesicles selected from the group consisting of Turicibacterales, Bifidobacteriales, Bacteroidales, and Enterobacteriales, ,
Such as Helicobacteraceae, Bacteroidaceae, Turicibacteraceae, Veillonellaceae, Bifidobacteriaceae, Barnées lacrosis, Bacillus thuringiensis, , Which is composed of Barnesiellaceae, Rikenellaceae, Clostridiaceae, Odoribacteraceae, Enterobacteriaceae, and Porphyromonadaceae. One or more family bacterial-derived extracellular vesicles selected from the group consisting of
But are not limited to, Trabulsiella, Enterobacter, Veillonella, Bifidobacterium, Lachnospira, Comamonas, Bacteroides, Such as Turicibacter, Sutterella, Klebsiella, Roseburia, Dialister, Ruminococcus, Parabacteroides, Butyricimonas, and Odoribacter, is diagnosed as lung cancer when the content of at least one genus bacterium-derived extracellular vesicle is decreased. .
3. The method of claim 2,
Wherein the blood is whole blood, serum, plasma, or blood mononuclear cells.

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