JP6896258B2 - How to detect early colorectal cancer - Google Patents

Description

The present invention relates to a method for detecting early colorectal cancer. If colorectal cancer can be detected early, not only can the mortality rate of the patient be reduced, but also the cancer can be removed endoscopically, so that the quality of life of the patient can be improved.

It is known that about 1000 types and 100 trillion bacteria coexist in the human intestine. These are called "intestinal flora", and recent reports have revealed that changes and abnormalities in bacterial composition, such as which bacterial species are present and how much, affect diseases and health conditions. Regarding colorectal cancer, the relationship with the intestinal flora has been pointed out for some time. For example, in Westerners, a paper report showing that Fusobacterium nucleatum is abundant in patients with advanced colorectal cancer has been reported. (Non-Patent Documents 1 and 2).

Conventionally, in order to investigate intestinal bacteria, it has been necessary to isolate the bacteria, culture them, and grow them. In recent years, a method has been developed to reconstruct the bacterial composition in a sample by extracting the DNA of the bacteria in the environmental sample and decoding the gene sequence information without going through the process of culturing (metagenomic analysis). .. By applying this to feces, it became possible to clarify the composition of the intestinal flora in the human intestinal environment. In addition, by using a mass spectrometer that quantitatively measures low-molecular-weight compounds (metabolites) in the environment, it is possible to obtain comprehensive data on metabolites in feces (metabolome analysis).

Zeller G et al., Mol Syst Biol. 2014 Nov 28; 10: 766 Feng Q et al., Nat Commun. 2015 Mar 11; 6: 6528

Early detection of colorectal cancer not only reduces mortality, but also improves the quality of life of patients, as most early-stage colorectal cancer can be removed endoscopically. It is also important from the point of view. Against this background, it is an object of the present invention to provide a means for detecting early colorectal cancer.

As a result of diligent studies to solve the above problems, the present inventor has a large composition of genes, microorganisms, and metabolites in the feces of patients with early-stage colorectal cancer as the composition in feces of patients with advanced colorectal cancer. It is different, and it was found that early colorectal cancer could not be detected by using markers for detecting advanced colorectal cancer, and further, a marker that could specifically detect early colorectal cancer was found, and based on these findings. , The present invention has been completed.

That is, the present invention provides the following [1] to [6].
[1] A method for detecting early colorectal cancer using the amount of a gene in the feces of a subject as an index, and the gene is a horse uric acid hydrolase gene, a cyclohexadienyl dehydratase gene, a butanol dehydrogenase gene, a nucleic acid base: a cation cotransporter. A method for detecting early-stage colorectal cancer, which is at least one selected from the group consisting of the -1 gene and the 3-hydroxy-5-phosphonooxypentane-2,4-dionethiolase gene.

[2] The genes are horse uric acid hydrolase gene, cyclohexadienyl dehydratase gene, butanol dehydrogenase gene, nucleobase: cation cotransporter-1 gene, and 3-hydroxy-5-phosphonooxypentane-2,4-dionthio. The method for detecting early-stage colorectal cancer according to [1], which is characterized by being a lase gene.

[3] A method for detecting early colorectal cancer using the amount of microorganisms in the feces of a subject as an index, which is characterized in that the microorganism is Bilophila wadsworthia [1] or [2]. The method for detecting early-stage colorectal cancer described in 1.

[4] A method for detecting early colorectal cancer using the amount of metabolites in the feces of a subject as an index, characterized in that the metabolites are glycocholic acid and / or taurocholic acid [1] to. The method for detecting early colorectal cancer according to any one of [3].

[5] The method for detecting early colorectal cancer according to any one of [1] to [4], which comprises the following steps (1) to (3).
(1) A step of quantifying a gene, a microorganism, or a metabolite as an index in the feces of a subject,
(2) A step of comparing the value quantified in step (1) with the corresponding value in the feces of a healthy person,
(3) As a result of comparison of step (2), if the value quantified in step (1) is statistically significantly higher than the corresponding value in the feces of a healthy subject, the subject is judged to have early colorectal cancer. Process to do.

[6] A method for detecting early colorectal cancer using the amount of microorganisms in the feces of a subject as an index, wherein the microorganism is Atopobium parvulum.

This specification includes the contents described in the Japanese patent application, Japanese Patent Application No. 2018-018134, and / or drawings which are the basis of the priority of the present application.

INDUSTRIAL APPLICABILITY According to the present invention, early detection of colorectal cancer, which was previously impossible without endoscopy, can be performed by non-invasive stool examination.

The relative abundance of genera (upper) and metabolites (lower) contained in the fecal samples of 318 patients is shown. The top 30 genera and metabolites with the highest abundance are shown. The breakdown of patients was "healthy" ("normal" and "small number of polyps") (N = 128), "multiple polyps" (N = 128), and "stage 0" (N =). 28), "Stage I / II" (N = 69), "Stage III / IV" (N = 54). Human genome fragments are also shown. Principal component analysis of genera (left) and metabolites (right). A taxonomic characteristic that shows differences as the cancer progresses. Species are determined by an increase or decrease in each of the four stages of "multiple polyps", "stage 0", "stage I / II", and "stage I / II" compared to "healthy" controls. (P value <0.05 in U test of one-sided Mann-Whitney). Species are shown in phylogenetic trees classified in the phyla of "Firmicutes", "Proteobacteria", "Bacteroidetes", "Actinobacteria", "Fusobacteria", "Tenericutes", and "Euryarchaeota". In the outer circle, the seeds are marked as increasing (orange) or decreasing (green). In particular, species with significantly increased (FDR-corrected P-value <0.05) are marked in red. The innermost circle shows the relative abundance of the species. For each phylum, it is widely distributed in species whose abundance has changed specifically for each stage (Private, red), species whose abundance has changed in common with other stages (Shared, blue), or all stages. The number of species whose abundance changed (Ubiquitous, green) was counted. Box plots (black boxes) show "healthy" controls and the relative abundance of representative species in four different groups (*, P <0.05; **, P <0.01.). Metabolic properties that show differences as the cancer progresses. Metabolites are determined by an increase or decrease in each of the four stages of "multiple polyps", "stage 0", "stage I / II", and "stage I / II" compared to "healthy" controls. (P value <0.05 in U test of one-sided Mann-Whitney). Based on metabolomics analysis, metabolites of major metabolic pathways, especially bile acids, short chain fatty acids (SCFA), and amino acids are shown. These metabolites show statistically significant differences between "healthy" controls and other groups. DCA stands for deoxycholic acid. Significant increases or decreases are shown as follows: ++ increases at P <0.01, + increases at P <0.05,-decreases at P <0.01,-decreases at P <0.05. Representative endoscopic images of "stage 0" colorectal cancer (CRC) before and after endoscopic mucosal incision (left), and representative preoperative endoscopic images of "stage III" CRC and the same patient Macroscopic image of the resected specimen (right). Metagenomics and metabolomics markers for detecting patients with "stage 0" and "stage III / IV" cancers. Species alone (red), KO alone (blue), metabolites alone (black) to classify "stage 0" (left) and "stage III / IV" (right) patients for "healthy" controls ) Or a combination of three features (green) to design a random forest model. In the "stage 0" classification, the species-based model used 7 species, the KO-based model used 5 KO, and the metabolite-based model used 5 metabolites. In the "Stage III / IV" classification, 17 species-based models were used, 15 KOs were used for KO-based models, and 20 metabolites were used for metabolite-based models. The combined model used the same features together. The classification accuracy was evaluated by leave-one-out verification in the area under the receiver operating characteristic curve (AUC). Metagenomics and metabolomics markers for detecting patients with "stage 0" and "stage III / IV" cancers. Contribution to the classifier is calculated using the Mean decrease Gini. The top 20 features of the whole are shown (left: stage 0, right: stage III / IV). The color of the box plot represents an increase (red) and a decrease (light blue) in each group compared to the "healthy" control. A pathway module for the biosynthesis and degradation of branched chain amino acids (BCAAs), aromatic amino acids (AAAs), methane, and hydrogen sulfide. Significantly increased or decreased (P <0.05 by unilateral Mann-Whitney U test) KO is shown in the pathway module. "Valine, leucine and isoleucine biosynthesis" (map00290) and "valine, leucine and isoleucine degradation" (map00280) in (a), and "phenylalanine, tyrosine and tryptophan biosynthesis" (map00400), "tryptophan metabolism" in (b). (Map00380), "tyrosine metabolism" (map00350) and "phenylalanine metabolism" (map00360), the pathway modules were modified from the KEGG pathway map. Three major methanogenic pathways (M00563, M00356 and M00567) (d) and genotoxic hydrogen sulfide production pathways (c) are shown. The figure which shows the detail of "valine, leucine and isoleucine decomposition" in FIG. 4A. For each KEGG ortholog (KO), bar graphs are in five groups, namely "healthy", "multiple polyps", "stage 0", "stage I / II" and "stage III / IV" samples. It shows the average abundance of KO and is colored according to the order of the values. KO is composed of organisms whose gene abundance is indicated by a circle. "Healthy" controls show the three most abundant organisms. The size and color of those circles is proportional to the relative abundance of genes in each group. The organism name is abbreviated as follows: bth, Bacteroides thetaiotaomicron VPI-5482; kpt, Klebsiella pneumoniae subsp. Pneumoniae ATCC 43816 KPPR1. Average abundances are also shown for Phe, Tyr, Trp, Val, Leu and Ile. The figure which shows the detail of "valine, leucine and isoleucine biosynthesis" in FIG. 4A. For each KEGG ortholog (KO), bar graphs are in five groups, namely "healthy", "multiple polyps", "stage 0", "stage I / II" and "stage III / IV" samples. It shows the average abundance of KO and is colored according to the order of the values. KO is composed of organisms whose gene abundance is indicated by a circle. "Healthy" controls show the three most abundant organisms. The size and color of those circles is proportional to the relative abundance of genes in each group. The organism name is abbreviated as follows: bth, Bacteroides thetaiotaomicron VPI-5482; kpt, Klebsiella pneumoniae subsp. Pneumoniae ATCC 43816 KPPR1. Average abundances are also shown for Phe, Tyr, Trp, Val, Leu and Ile. The figure which shows the detail of "tyrosine metabolism" in FIG. 4A. For each KEGG ortholog (KO), bar graphs are in five groups, namely "healthy", "multiple polyps", "stage 0", "stage I / II" and "stage III / IV" samples. It shows the average abundance of KO and is colored according to the order of the values. KO is composed of organisms whose gene abundance is indicated by a circle. "Healthy" controls show the three most abundant organisms. The size and color of those circles is proportional to the relative abundance of genes in each group. The organism name is abbreviated as follows: bth, Bacteroides thetaiotaomicron VPI-5482; kpt, Klebsiella pneumoniae subsp. Pneumoniae ATCC 43816 KPPR1. Average abundances are also shown for Phe, Tyr, Trp, Val, Leu and Ile. The figure which shows the detail of "phenylalanine metabolism" in FIG. 4A. For each KEGG ortholog (KO), bar graphs are in five groups, namely "healthy", "multiple polyps", "stage 0", "stage I / II" and "stage III / IV" samples. It shows the average abundance of KO and is colored according to the order of the values. KO is composed of organisms whose gene abundance is indicated by a circle. "Healthy" controls show the three most abundant organisms. The size and color of those circles is proportional to the relative abundance of genes in each group. The organism name is abbreviated as follows: bth, Bacteroides thetaiotaomicron VPI-5482; kpt, Klebsiella pneumoniae subsp. Pneumoniae ATCC 43816 KPPR1. Average abundances are also shown for Phe, Tyr, Trp, Val, Leu and Ile. The figure which shows the detail of "tryptophan metabolism" in FIG. 4A. For each KEGG ortholog (KO), bar graphs are in five groups, namely "healthy", "multiple polyps", "stage 0", "stage I / II" and "stage III / IV" samples. It shows the average abundance of KO and is colored according to the order of the values. KO is composed of organisms whose gene abundance is indicated by a circle. "Healthy" controls show the three most abundant organisms. The size and color of those circles is proportional to the relative abundance of genes in each group. The organism name is abbreviated as follows: bth, Bacteroides thetaiotaomicron VPI-5482; kpt, Klebsiella pneumoniae subsp. Pneumoniae ATCC 43816 KPPR1. Average abundances are also shown for Phe, Tyr, Trp, Val, Leu and Ile. The figure which shows the detail of "phenylalanine, tyrosine and tryptophan biosynthesis" in FIG. 4A. For each KEGG ortholog (KO), bar graphs are in five groups, namely "healthy", "multiple polyps", "stage 0", "stage I / II" and "stage III / IV" samples. It shows the average abundance of KO and is colored according to the order of the values. KO is composed of organisms whose gene abundance is indicated by a circle. "Healthy" controls show the three most abundant organisms. The size and color of those circles is proportional to the relative abundance of genes in each group. The organism name is abbreviated as follows: bth, Bacteroides thetaiotaomicron VPI-5482; kpt, Klebsiella pneumoniae subsp. Pneumoniae ATCC 43816 KPPR1. Average abundances are also shown for Phe, Tyr, Trp, Val, Leu and Ile. The figure which shows the detail of the production pathway of genotoxic hydrogen sulfide in FIG. 4A. For each KEGG ortholog (KO), bar graphs are in five groups, namely "healthy", "multiple polyps", "stage 0", "stage I / II" and "stage III / IV" samples. It shows the average abundance of KO and is colored according to the order of the values. KO is composed of organisms whose gene abundance is indicated by a circle. "Healthy" controls show the three most abundant organisms. The size and color of those circles is proportional to the relative abundance of genes in each group. The organism name is abbreviated as follows: bth, Bacteroides thetaiotaomicron VPI-5482; kpt, Klebsiella pneumoniae subsp. Pneumoniae ATCC 43816 KPPR1. Average abundances are also shown for Phe, Tyr, Trp, Val, Leu and Ile. FIG. 4A shows details of the three major pathways of methanogenesis. For each KEGG ortholog (KO), bar graphs are in five groups, namely "healthy", "multiple polyps", "stage 0", "stage I / II" and "stage III / IV" samples. It shows the average abundance of KO and is colored according to the order of the values. KO is composed of organisms whose gene abundance is indicated by a circle. "Healthy" controls show the three most abundant organisms. The size and color of those circles is proportional to the relative abundance of genes in each group. The organism name is abbreviated as follows: bth, Bacteroides thetaiotaomicron VPI-5482; kpt, Klebsiella pneumoniae subsp. Pneumoniae ATCC 43816 KPPR1. Average abundances are also shown for Phe, Tyr, Trp, Val, Leu and Ile. Relationship between Bilophila wadsworthia and DCA. Comparison of relative abundance of species before and after surgery. The X-axis shows the log fold change, and the Y-axis shows the P value by Wilcoxon signed rank test (left). The size of the circle indicates the various abundances averaged in the preoperative and postoperative conditions. Species previously allegedly increased or decreased in colorectal cancer (CRC) (reference 12) are highlighted in red and blue, respectively. The names of species with low P-values are grayed out. The abundances of Fusobacterium nucleatum, Peptostreptococcus stomatis and Bilophila wadsworthia are compared before and after surgery (right). The increasing and decreasing patterns are colored red and blue, respectively. In addition, a box plot (black) of the abundance of these bacteria in 30 post-surgery patients with CRC for which pre-surgery stool samples were not available is also shown. Relative abundance of Atopobium parvulum contained in each sample. Relative abundance of Bacteroides coprocola contained in each sample. Relative abundance of Bacteroides plebeius contained in each sample. Relative abundance of Bifidobacterium breve contained in each sample. Relative abundance of Bifidobacterium choerinum contained in each sample. Relative abundance of Bifidobacterium dentium contained in each sample. Relative abundance of Bifidobacterium mouka labense contained in each sample. Relative abundance of Bifidobacterium pseudolongum subsp. Globosum contained in each sample. Relative abundance of Bifidobacterium stellenboschense contained in each sample. Relative abundance of Eubacterium eligens contained in each sample. Relative abundance of Fusobacterium nucleatum subsp. Fusiforme contained in each sample. Relative abundance of Fusobacterium nucleatum subsp. Nucleatum contained in each sample. Relative abundance of Fusobacterium nucleatum subsp. Vincentii contained in each sample. Relative abundance of Lactobacillus rogosae contained in each sample. Relative abundance of Parabacteroides gordonii contained in each sample. Relative abundance of Roseburia intestinalis contained in each sample. Relative abundance of Solobacterium moorei contained in each sample. Relative abundance of Atopobium parvulum. A: Relative atopobium parvulum abundance (metagenomic analysis) in healthy subjects and colorectal cancer patients at each stage. B: Relative atopobium parvulum abundance (metagenomic analysis) in healthy subjects and stage 0 colorectal cancer patients. C: Relative atopobium parvulum abundance (qPCR, bar graph) in healthy subjects and stage 0 colorectal cancer patients. D: Scatter plot showing that there is no difference between metagenomic analysis and qPCR. Dots indicate samples. E: Relative atopobium parvulum abundance (qPCR, box plot) between healthy individuals and patients with stage 0 colorectal cancer.

Hereinafter, the present invention will be described in detail.
The method for detecting early colorectal cancer of the present invention is a method for detecting early colorectal cancer using the amount of a gene in the feces of a subject as an index, and the genes are a horseuric acid hydrolase gene, a cyclohexadienyl dehydratase gene, and a butanol dehydrogenase gene. , Nucleic acid base: At least one selected from the group consisting of the cation cotransporter-1 gene and the 3-hydroxy-5-phosphonooxypentane-2,4-dionethiolase gene. is there.

Here, "early colorectal cancer" refers to colorectal cancer other than advanced colorectal cancer (for example, stage III and IV colorectal cancer), and mainly means stage 0 colorectal cancer.

The gene used for the detection of early colorectal cancer may be any one selected from the above five genes, any two genes, any three genes, or any four genes. When detecting early colorectal cancer with high accuracy, it is preferable to use all five types.

It can also be combined with other indicators, such as the amount of microorganisms and metabolites in the faeces, to improve detection accuracy. Examples of microorganisms include Bilophila wadsworthia and Atopobium parvulum, and examples of metabolites include glycocholic acid and / or taurocholic acid. The detection of early colorectal cancer using the amount of microorganisms or the amount of metabolites as an index may be performed independently without combining with the method using the amount of genes as an index.

Specific examples of the method for detecting early-stage colorectal cancer of the present invention include methods including the steps (1) to (3) described below.

In step (1), the gene used as an index in the feces of the subject is quantified. At this time, if the amount of microorganisms and the amount of metabolites are also used as indicators, they are also quantified. Genes can be quantified by any method, for example, total DNA can be extracted from fecal samples, their sequences can be read, and quantification can be performed based on the sequences and a database of genes (for example, KEGG). is there. Microorganisms can be quantified, for example, by extracting total DNA from a stool sample, reading their sequences, and based on that sequence and the 16S ribosomal RNA database (eg, SILVA). The quantification of metabolites can be performed using CE-MS or the like.

In step (2), the value quantified in step (1) is compared with the corresponding value in the feces of a healthy person. The corresponding value in the feces of a healthy person may be determined before the quantification of the feces of the subject, or may be determined at the same time as the quantification of the feces of the subject.

In step (3), as a result of comparison in step (2), if the value quantified in step (1) is statistically significantly higher than the corresponding value in the feces of a healthy subject, the subject is treated with early-stage colorectal cancer. Judge that there is. As shown in Examples described later, the abundance of the above-mentioned genes, microorganisms, and metabolites in feces was increased in patients with early-stage colorectal cancer as compared with healthy subjects. ) Is statistically significantly higher than the corresponding value in the feces of healthy subjects, the subject is likely to have early colorectal cancer. Here, "statistically significantly higher" means that there is a significant difference between the numerical values obtained from each of the subject and the healthy subject when statistically processed. As the test method for statistical processing, a known test method capable of determining the presence or absence of significance may be appropriately used, and is not particularly limited, and for example, a Student's t-test method or a multiple comparison test method can be used.

Hereinafter, the present invention will be described in more detail by way of examples, but the present invention is not limited to this example.

[Example 1]
(A) Materials and methods The samples and clinical information used in this study were obtained with the approval of informed consent and the facility review committee of each facility.

(1) Subjects and sample collection This study is conducted on subjects undergoing total colonoscopy at the National Cancer Center Hospital. The dietary habits and other lifestyle habits of the subjects were obtained from a detailed questionnaire (question items 475, p. 25) based on the questionnaire used in the Japan Public Health Center (JPHC) survey (references 1 and 2). Fecal samples were taken immediately upon first defecation during oral administration of bowel irrigation in the hospital and frozen on dry ice. This is because it has been proved that such a sample shows a high correlation with a sample (standard sample) taken before colonoscopy (Reference 3).

Feces were collected from 2,474 subjects who underwent colonoscopy at the National Cancer Center Hospital from February 2014 to December 2017. Metagenomic data were collected from 484 subjects. Metabolome data were collected from 517 patients. Some subjects were excluded from this study. Whether these subjects were diagnosed with a hereditary disorder (eg, familial adenomatous polyposis, hereditary nonpolyposis colorectal cancer), had a history of gastric cancer surgery, or their fecal samples were collected for data. It wasn't enough. Finally, a total of 348 subjects (204 males and 144 females, ages 28-79, with an average age of 63.3 years) with data from both metagenomic and metabolome analysis. Was registered in this study. Subjects were divided into the following nine groups based on colonoscopy findings.
1. Normal (not noticeable on colonoscopy)
2. A few polyps (up to 2 small (less than 5 mm) polyps)
3. Multiple polyps (3 or more polyps, mainly 5 or more polyps)
4. Stage 0 CRC
5. Stage I CRC
6. Stage II CRC
7. Stage III CRC
8. Stage IV CRC
9. No significant findings after CRC surgery

(2) DNA extraction
DNA was extracted from stool samples frozen by the bead beating method using the GNOME DNA Isolation Kit (MP Biomedicals, Santa Ana, CA) as previously described (Reference 4). DNA quality was analyzed on an Agilent 4200 TapeStation (Agilent Technologies, Santa Clara, CA). After final precipitation, DNA was resuspended in TE buffer and stored at -80 ° C for further analysis.

(3) Metagenomic analysis sequencing library was generated using the Nextera XT DNA Sample Prep Kit (Illumina, San Diego, CA) according to the supplier's recommendations with the following modifications: DNA starting amount 500 ng, in the process Kit chemical dilution 1: 1. The quality of the library was analyzed on an Agilent 4200 Tape Station. Whole-genome shotgun sequencing of fecal samples was performed on the HiSeq 2500 platform (Illumina). All samples were paired-end sequenced with a read length of 150 bp to an average target depth of 6.0 Gbps.

Total DNA was extracted from fecal specimens and a total of 48,261,700 read data from high quality Illumina metagenomic shotgun sequencing was aligned with the 16S ribosomal RNA database (SILVA LTP) to identify 9,352 species (2,123 genera). High quality read data is newly assembled.

A total of 79,278,379 predicted open reading frames (ORFs) are included in the Integrated Reference Catalog of the Human Gut Microbiome (IGC) (Reference 5) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Reference 6). Aligned and identified 6,428 KEGG orthologs (KOs).

(3) Metabolome analysis As previously reported (Reference 7), quantitative analysis of charged metabolites by CE-TOFMS was performed. Fecal metabolites were extracted by vigorous shaking with methanol containing 20 μM each of methionine sulfone and D-camfur-10-sulfonic acid as internal standards (Reference 8). All CE-TOFMS experiments were performed using an Agilent CE capillary electrophoresis system. Principal component analysis of CE-TOFMS data was performed using SIMCA13.0 (Umetrics, Umea, Sweden). For analysis, sub-detection limit concentrations were replaced with zero to exclude sub-detection limit levels of metabolites in all samples.

(4) Statistical analysis Patients with "Stage I CRC" and "Stage II CRC" are combined with "Stage I / II", and patients with "Stage III CRC" and "Stage IV CRC" are combined with "Stage III / IV". Combined. Species / KO / metabolite amounts were defined to be significantly increased or decreased when the P value was less than or equal to 0.05 in the unilateral Mann-Whitney U test.

(B) Results (1) Subjects
348 subjects had "normal" (N = 55), "few polyps" (N = 73), and "multiple polyps" in addition to "significant findings after CRC surgery" (N = 30). (N = 39), "Stage 0 CRC" (N = 28), "Stage I CRC" (N = 44), "Stage II CRC" (N = 25), "Stage III CRC" (N = 35), And "Stage IV CRC" (N = 20) was diagnosed. Patients with "normal" and "small number of polyps" were defined as "healthy" controls (N = 128). In addition, metagenomic data before and after treatment were obtained from 10 patients with "Stage I CRC", 7 patients with "Stage II CRC", and 8 patients with "Stage III CRC".

(2) Overview of metagenomic data and metabolome data
The overall picture of metagenomic and metabolome data for 318 subjects (excluding 30 patients with CRC surgery history) is "healthy,""small number of polyps,""multiplepolyps," and ". It is shown in "Stage 0", "Stage I / II", and "Stage III / IV" (FIGS. 1A and 1B). Subjects rich in Bacteroides bacteria were in the opposite relationship to subjects rich in Prevotella bacteria. Notably, the genus Megamonas was detected as a very common genus (in the 10th most common genus) in 56 patients (17.6%) in all groups, but barely in the rest of the subjects. , Showed an unusual distribution. Human genome content was higher at advanced CRC stages compared to "healthy" controls (P = 2.1e-5 for "stages I / II"; P = 2.2e-7 for "stages III / IV" ). Principal component analysis (PCA) shows two variability clusters (ie, Bacteroides and Prevotella) (Fig. 1B left). These two bacteria are defined as human intestinal enterotypes (Reference 9). Interestingly, the Prevotella cluster was dominated by Japanese males (72% of subjects in the Prevotella cluster, P = 0.0032 in the χ2 study). Propionic acid and butyric acid are the major sources of energy for the large intestine (Reference 10), but these two were the most abundant metabolites. PCA shows that dihydrouracil and urea had large fluctuations in the population (Fig. 1B right).

(3) Changes in gut microbiota in adenoma-carcinoma sequence One-sided Mann-Whitney U-test in "multiple polyps", "stage 0", "stage I / II", and "stage III / IV" subjects The increase or decrease in the relative abundance of the species was examined in comparison to the "healthy" control (Fig. 2A). In addition to the more advanced stages of multistage carcinogenesis (“Stage I / II” and “Stage III / IV”), the present inventor also has “multiple polyps” and carcinoma in situ (“Stage 0”). We have found that changes in the intestinal flora appear. More importantly, changes in the gut microbiota varied significantly from stage to stage. Species belonging to the phylums Actinobacteria, Fusobacteria, Tenericutes, and Euryachaeota showed marked increases and decreases in "Stage 0", "Stage I / II" and "Stage III / IV", but "multiple polyps". There was almost no difference. This indicates that these phyla are almost unchanged in prodromal lesions. Bifidobacteria decreased only in "Stage I / II". Certain classes of Firmicutes (eg Clostridia and Erysipelotrichia) increased primarily in "Stage III / IV". According to previous studies (references 11 and 12), Fusobacterium nucleatum spp. (Eg, F. nucleatum subsp. Nucleatum (FDR-corrected P = 9.5e-6)), Peptostreptococcus stomatis (P = 5.3e-6). , And the relative abundance of Parvimonas micra (P = 3.4e-5) increased in "Stage III / IV". Interestingly, species of the genus Desulfovibrio (eg, D. vietnamensis (P = 0.033)) increased in "stage 0". In addition, another species that was significantly elevated in "Stage 0" was Bilophila wadsworthia (P = 0.039), a closely related species of Desulfovibrio spp. (Reference 13). These bacteria are well known as sulfide-producing bacteria (sulfates are not used, but sulfates and thiosulfates are used as electron acceptors). In contrast, butyric acid-producing bacteria, such as Faecalibacterium prausnitzii (“multiple polyps”, P = 0.0009; “stage 0”, P = 0.023; “stage III / IV”, P = 0.031), Roseburia intestinalis ( "Stage 0", P = 0.0026; "Stage III / IV", P = 0.0009), Eubacterium eligens ("Multiple polyps", P = 0.024; "Stage 0", P = 0.013; "Stage I / II", P = 0.029; "Stage III / IV", P = 0.0094), Lachnospira multipara ("Multiple polyps", P = 0.030; "Stage 0", P = 0.0007; "Stage I / II", P = 0.0046; Stage III / IV ”, P = 0.0005) decreased in the prodromal stage and the cancer stage. These findings indicate that the intestinal microbial composition changed dramatically during multistage carcinogenesis. Recently, the relative abundance of Akkermansia muciniphia has been shown to correlate with clinical responses to immune checkpoint inhibitors (reference 14) in addition to obesity and diabetes (reference 15). Bacteria were significantly increased in stage III / IV (P = 0.028) (reference 16).

(4) Changes in functional characteristics of microbial genes in CRC carcinogenesis A significant increase or decrease in the abundance of KO at each stage was investigated in comparison with "healthy" controls. As a result, at any stage, 1,497 KOs showed a significant increase and 887 KOs showed a significant decrease. Twenty-three of these KOs showed both ups and downs at different stages (P <0.05 in the unilateral Mann-Whitney U test). In general, KO of lysine metabolism and methane metabolism increased significantly in "Stage III / IV". On the other hand, KO of arginine and proline metabolism decreased. Details will be explained in a later section.

(5) Changes in metabolome profile from early stages of carcinogenesis CE-TOFMS metabolome data were obtained for 517 compounds that increased or decreased at each cancer stage compared to "healthy" controls. At either stage, 218 metabolites showed statistical differences compared to "healthy" controls (P <0.05 in the unilateral Mann-Whitney U test). Among them, the present inventor focused on six categories of metabolites closely related to the intestinal microflora: bile acids, short-chain fatty acids (SCFA), vitamins, amino acids, polyamines, and central carbon metabolism (Fig. 2B). ) (Reference 17). First, the present inventor paid attention to the concentration of bile acid in feces. Deoxycholic acid (DCA) (P = 0.0002) and glycocholic acid (P = 0.0385) were increased in "multiple polyps". Glycocholic acid (P = 0.0078) and taurocholic acid (P = 0.0047) increased in "stage 0". Interestingly, DCA decreased in "Stage III / IV" compared to "healthy" controls (P = 0.0183). Next, the inventor focused on changes in amino acid (AA) concentration in feces. Fifteen amino acids were increased at all CRC stages. 9 amino acids increased in "Stage 0", 8 amino acids increased in "Stage I / II", and 12 amino acids increased in "Stage III / IV" (Fig. 2B). On the other hand, "multiple polyps" increased only 2 amino acids. Aspartic acid (Asp) decreased at "stage 0" (P = 0.0448). Notably, the levels of branched-chain amino acids (BCAA: isoleucine, leucine, valine) increased in most of the CRC stages. In addition, the inventor has focused on metabolites in the TCA cycle. An increase in fumaric acid, succinic acid and malic acid concentrations was observed at "stage 0". The first two metabolites were also increased in "Stage III / IV". These findings indicate bacterial fumaric acid respiration in the intestine of CRC patients (Reference 31).

(6) Metagenome and metabolome approaches to detect early stages of CRC Most of the "stage 0" CRC can be treated by an endoscopic approach (Fig. 3A), with a wide time frame for detection. is there. Therefore, it is important to detect such early stage CRC to prevent CRC mortality. To explore the potential of intestinal metagenomics and metabolomics data as diagnostic markers to distinguish "stage 0" and "stage III / IV" from "healthy" controls, we constructed a random forest classifier. did. Leave-one-out verification was performed. Four types of models were constructed based on either species only, KO only, metabolites only, or a combination of three species. Known risk factors (ie smoking and alcohol) and fragments of the human genome were added to the features. Comparing the classification potentials among the four models, the combined model was superior in both the "Stage 0" (Fig. 3B, left) and "Stage III / IV" (Fig. 3B, right) classifications. The inventor preserved a 0.85 receiver operating characteristic curve sub-curve (AUC) region for detecting "stage 0" patients and a 0.87 region for "stage III / IV" patients. The contribution of each marker was calculated using the Mean Decrease Gini (Fig. 3C). The following KOs were ranked high in the properties that characterize "stage 0": K01451 (hipO, hippurate hydrolase), K01713 (pheC, cyclohexadienyl dehydratase), K00100 (bdhAB, butanol dehydrogenase), K03457 (TCNCS1,) Nucleic acid base: cation cotransporter-1, NCS1 family), KO8321 (lsrF, 3-hydroxy-5-phosphonooxypentane-2,4-dionethiolase). The major complex gene for K01451 was derived from Klebsiella pneumonia at "stage 0". K01713 is a biosynthetic enzyme of phenylalanine (Phe).

The "Stage III / IV" classifiers were mostly characterized by species (Fig. 3B, right). The top-ranking species were mainly oral anaerobes such as P. stomatis, F. nucleatum, Peptostreptococcus anaerobius, and P. micra. These bacteria have long been identified as CRC marker species (references 11, 12, 19, 20). The human genome fragment was a highly rated property that distinguishes "stage III / IV" patients from "healthy" controls.

(7) Changes in KO involved in the microbial metabolic pathway Among the many pathways, the present inventor focused on three pathways (ie, amino acids, methane and sulfur). This is because these pathways are known to be closely related to the microbial flora (Reference 21). As mentioned in the previous section, amino acid concentrations increased predominantly at all stages of CRC, based on metabolomics analysis. The present inventor investigated changes in the abundance of KOs for which route information is available. As a result, an interesting pattern of KO changes was observed. KOs with increased abundance at the CRC stage had more amino acid degradation compared to "healthy" controls. In contrast, KOs reduced at the CRC stage were more amino acid biosynthetic. KOs other than K10797 showed neither an increase nor a decrease. In general, the abundance of KO involved in amino acid biosynthesis was two orders of magnitude higher than that of amino acid degradation. This is a point to be aware of. For example, many KOs involved in BCAA biosynthesis (K00052, K00053, K01703, K01704, K01652, K01653, K01687, K01754) were reduced, while KOs involved in their degradation pathways (K00166, K00166, K00167, K00253, K00263, K01692, K11381) increased (FIGS. 4A to 4G). These findings can be interpreted as an increase in host (human) -derived BCAA microbial consumption with a decrease in BCAA microbial biosynthesis and an increase in BCAA microbial degradation. The inventor also focused on increasing KO in the metabolism of aromatic amino acids (AAA), including the Phe metabolic pathway. This is because this metabolism is predominant in "stage 0" and K01713 has been positioned as a diagnostic marker that distinguishes "stage 0" from "healthy" controls (Fig. 3C). In the Phe metabolic pathway, Phe can be metabolized to phenylacetic acid (PAA) (C07086, metabolome data not available) by either human or bacterial enzymes. Such enzymes include K00832 (P = 0.042), K00276 (P = 0.042), K01426 (P = 0.042), K00055 (P = 0.027), K00457 (P = 0.0031), all of which It increased at "Stage 0" (Figs. 4A-G). PAA is excreted in the urine by glucuronidation or is catalyzed by gene clusters (paaK → paaA / paaB / paaC / paaD / paaE → paaG → paaZ → paaG / paaJ → paaF → paaH → paaJ). It can be further metabolized by bacteria via the reaction (Reference 22). Among these genes, those increased in "stage 0" were paaG (K15866) (P = 0.029), paaJ (K02615) (P = 0.027) and paaF (K01692) (P = 0.0093). The major complex of paaG (K15866) was Megamonas hypermegale. The complex species of paaJ (K02615) and paaF (K01692) were Escherichia coli K-12 MG1655 and Klebsiella pneumoniae subsp. Pneumoniae MGH 78578. The AAA biosynthetic pathway showed both an increase and a decrease in KO. Two Phe biosynthetic genes, namely cyclohexadienyl dehydratase (pheC, K01713) (P = 5.1e-06) and aromatic amino acid transaminase (tyrB, K00832) (P = 0.0414), were significantly at "stage 0". Increased. K00832 is also involved in the biosynthesis / degradation of other amino acids. Other enzymes involved in the Trp biosynthetic pathway were largely reduced in "Stage III / IV". Metabolome data showed that fecal Phe increased first in "stage 0" and remained at similar levels of increase in "stage I / II" and "stage III / IV" (Fig. 2B).

The relationship between CH ₄ (methane) and CRC remains controversial, but numerous studies have discussed the effects / roles of methane gas on CRC (Reference 23). A significant increase in a large number of methanogenic KOs was observed at "Stage III / IV". Such KOs include K00390 cysH, phosphoadenosine phosphosulfate reductase (P = 0.0071), which contains three methanogenic pathways: CO ₂ → CH ₄ , CH ₄ O → CH ₄ , Methylamine / Dimethylamine / Trimethylamine → CH ₄ involved (Fig. 4I). The major complex of these KOs is the archaea Methanobrevibacter smithii, which has been reported to be much less common in healthy Japanese than in other countries (Reference 24). Finally, the catabolic sulfite reductase α subunit (K11180) is involved in the production of genotoxic hydrogen sulfide, a protein that is "multiple polyps" (P = 0.0080), "stage 0" (P = 0.013). , And “Stage III / IV” (P = 0.0033) increased (Fig. 4H). This dominant gene for KO constitutes Desulfovibrio vulgaris, a sulfate-reducing bacterium.

(8) Changes in microbial composition before and after surgery in CRC patients
At 28 CRCs (Stages I / II / III / IV), metagenomic data were obtained before and after treatment (about 1 year after surgery) for the same patient. The present inventor compared the microbial composition before and after surgery. The relative abundance of F. nucleatum spp., P. stomatis, Porphyromonas asaccharolytica and M. smithii decreased after surgical tumor removal in "Stage I / II / III / IV" (Fig. 5B). Interestingly, Streptococcus salivarius subsp. Salivarius increased after surgery. This bacterium has previously been reported as a species that is reduced in "Stage III / IV" compared to controls (Reference 12).

(9) Relationship between Bilophila wadsworthia and DCA As mentioned earlier in this article, DCA increased with "multiple polyps" compared to "healthy" controls. To search for species that may correlate with this metabolite, we investigated the correlation between the species and the metabolite. As a result, Bilophila wadsworthia showed the highest correlation coefficient with DCA (CC = 0.641). The correlation coefficient was positive at other stages, but not as high as "multiple polyps". Bilophila wadsworthia is known to grow in a medium containing taurocholic acid (Reference 32). Taurocholic acid is a conjugate of the DCA precursor (cholic acid).

(C) Discussion The present inventor has conducted a large-scale study on the relationship between the intestinal environment and colorectal cancer by metagenomic analysis and metabolome analysis. Metabolome analysis provides new information on host and microbial metabolite profiles. All subjects, including "healthy" controls, underwent colonoscopy. Megamonas was detected as a very abundant genus in about 20% of Japanese in all groups, but interestingly not in the majority of subjects (about 80%). Megamonas has not been previously reported as a dominant genus in European and American gut microbiota studies, suggesting that Megamonas is a new enterotype, probably characteristic of Asian populations (Reference 26). ). The inventor has shown that in addition to the more advanced stages of multistage carcinogenesis, changes in the intestinal flora and metabolome have appeared in "plurality of polyps" and carcinoma in situ ("stage 0"). More importantly, changes in the gut microbiota varied significantly from stage to stage. At the precursor stage of CRC, DCA is significantly increased in faeces. DCA is known to cause increased DNA damage and increase mutations (reference 27), and animal studies have shown that administration of bile acids increases the incidence of tumors in the intestine (reference 27). References 27, 28). In addition, DCA has ^{recently been reported to cause mucosal cancers in the small and large intestines of APC Min / +} mice and multiple tumors in the liver of leptin-deficient (ob / ob) mice (references 29, 30). Although the growth of Bilophila wadsworthia is promoted by bile (Reference 13), this bacterium was the only species that was significantly correlated with DCA in this study (Fig. 5A). Desulfovibrio (eg, D. vietnamensis) and Bilophila wadsworthia (a closely related species of Desulfovibrio spp.) Were significantly increased in "stage 0". They are well known to be sulfide-producing bacteria (Reference 31) and have a catabolic sulfite reductase α subunit (K11180) involved in the production of genotoxic hydrogen sulfide. A meaty diet that promotes high levels of taurine conjugation causes overgrowth of sulfate-reducing bacteria such as Bilophila wadsworthia, which is associated with pathological intestinal conditions, such as tumorigenesis (references). 21, 32, 33). Bilophila wadsworthia has been studied for the formation of colitis in Il10-/-mice in the presence of taurocholic acid (Reference 32). Recently, Bilophila wadsworthia has been reported to be significantly more abundant in African-Americans with CRC than in healthy individuals (Reference 34). O'Keefe et al. Reported an increase in sulfide-producing bacteria in the colon when the Zulu tribe of rural South Africa, who normally had a low-fat, high-fiber diet, switched to a diet high in meat and animal fat ( Reference 35). Although these microorganisms are a normal part of the intestinal ecosystem, excesses of these bacteria and metabolites in the colorectal polyps can cause inflammation and damage DNA. Considered in conjunction with previous studies, Desulfovibrio and Bilophila wadsworthia were shown to contribute to early tumorigenesis, but were not experimentally validated in this study.

In this study, amino acids in faeces, especially BCAAs, were increased based on metabolome analysis. However, unexpectedly, in CRC, KOs involved in amino acid biosynthesis generally decreased, and KOs involved in amino acid degradation increased. This suggests that the increase in amino acids is derived from the host (mainly non-cancerous epithelial cells) and affects the microbial composition and / or gene function of the intestinal microflora from the very early stages of CRC. doing.

Fusobacterium nucleatum is well known to be involved in CRC (references 36, 37). In fact, F. nucleatum spp. Did not increase in the early stages of CRC, but increased in "Stage III / IV". However, the relative abundance of F. nucleatum spp. Was dramatically reduced after surgical removal of the tumor.

The present inventor's results suggest that F. nucleatum cannot enhance direct and specific carcinogenesis, but increases probably as a result of environmental changes due to CRC such as bleeding (Reference 38). .. This is the inventor's preliminary data that F. nucleatum was not increased in preoperative patients with familial adenoma-like polyposis compared to "healthy" controls (data not shown, N = 15). ). However, F. nucleatum and P. stomatis can be good biomarkers for detecting advanced CRC. In contrast, Bilophila wadsworthia showed an increase at "Stage 0" and did not decrease after surgery.

Fecal occult blood tests are routinely used for high-dose CRC screening and are performed prior to colonoscopy. Early detection of CRC, especially stage 0, allows endoscopic resection of the tumor and significantly improves the patient's quality of life. This study proposes an intestinal flora index for non-invasive cancer screening tests. Phe's biosynthetic enzyme, K01713, can distinguish "stage 0" patients from "healthy" controls. The fact that protein is a major component of meat and that fermented protein metabolites such as Phe and tryptophan metabolites are potentially carcinogenic is a possibility between meat intake, protein fermentation and CRC. It suggests a certain connection (Reference 39). Specifically, Phe metabolites such as PAA are considered harmful metabolites (references 40, 41).

In conclusion, we have observed microbial composition, gene abundance in the intestinal flora, and dynamic changes in host and microbial metabolites in multistage carcinogenesis. Structural changes in the gut microbiota can lead to changes in the carcinogenic microenvironment. In addition, the study emphasizes that CRC progression is affected by the presence of net metabolites and causative organisms throughout the microorganism. The inventor considers CRC to be not only a "genetic" but also a "microbial" disease.

[Example 2]
Metagenomic analysis was performed by increasing the number of samples to 616 samples. Specimens were used for healthy subjects (H), patients with multiple polyps (MP), stage 0 colorectal cancer patients (S0), stage I and II colorectal cancer patients (SI / II), and stage III and IV colorectal colons. The patients were divided into cancer patients (SIII / IV), and the abundance of bacteria contained in each sample was examined. The bacteria examined were Atopobium parvulum, Bacteroides coprocola, Bacteroides plebeius, Bifidobacterium breve, Bifidobacterium choerinum, Bifidobacterium dentium, Bifidobacterium moukalabense, Bifidobacterium pseudolongum subsp. There are 17 types of Fusobacterium nucleatum subsp. Vincentii, Lactobacillus rogosae, Parabacteroides gordonii, Roseburia intestinalis, and Solobacterium moorei. The results are shown in FIGS. 6 to 22.

As a result of the above analysis, it was clarified that Atopobium parvulum is abundant in stage 0 colorectal cancer patients. Therefore, we investigated this Atopobium parvulum in more detail (Fig. 23). As shown in FIG. 23A, the relative abundance of Atopobium parvulum was significantly higher in patients with stage 0 colorectal cancer. From this, it may be possible to determine whether or not it is early colorectal cancer by examining the abundance of Atopobium parvulum in the sample.

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All publications, patents and patent applications cited herein are incorporated herein by reference in their entirety.

The present invention is available in the industrial field related to the detection of colorectal cancer.

Claims (5)

Using the amount of genes in the feces of the subject as an index, if the amount of genes in the feces of the subject is larger than the amount of genes in the feces of a healthy person, the subject is judged to have stage 0 colon cancer, stage 0. A method for detecting colorectal cancer in the body, the genes of which are horse uric acid hydrolase gene, cyclohexadienyl dehydratase gene, butanol dehydrogenase gene, nucleic acid base: cation cotransporter-1 gene, and 3-hydroxy-5-phosphonooxy A method for detecting stage 0 colon cancer , which is at least one selected from the group consisting of the pentan-2,4-dionethiolase gene. The genes are horse uric acid hydrolase gene, cyclohexadienyl dehydratase gene, butanol dehydrogenase gene, nucleobase: cation cotransporter-1 gene, and 3-hydroxy-5-phosphonooxypentane-2,4-dionethiolase gene. The method for detecting stage 0 colorectal cancer according to claim 1, wherein the gene is present. Claim 1 or 2 is a method for detecting stage 0 colorectal cancer using the amount of metabolite in the feces of a subject as an index, wherein the metabolite is glycocholic acid and / or taurocholic acid. The method for detecting stage 0 colorectal cancer according to the above. The method for detecting stage 0 colorectal cancer according to any one of claims 1 to 3 , which comprises the following steps (1) to (3).
(1) A step of quantifying a gene or metabolite as an index in the feces of a subject,
(2) A step of comparing the value quantified in step (1) with the corresponding value in the feces of a healthy person,
(3) As a result of comparison in step (2), if the value quantified in step (1) is statistically significantly higher than the corresponding value in the feces of a healthy subject, the subject is stage 0 colorectal cancer . The process of determining. Using the amount of microorganisms in the feces of the subject as an index, if the amount of microorganisms in the feces of the subject is larger than the amount of microorganisms in the feces of a healthy person, the subject is judged to have stage 0 colorectal cancer, stage 0. A method for detecting stage 0 colorectal cancer , wherein the microorganism is Atopobium parvulum.

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