WO2018155960A1 - Procédé de diagnostic du cancer de l'ovaire par analyse du métagénome microbien - Google Patents

Procédé de diagnostic du cancer de l'ovaire par analyse du métagénome microbien Download PDF

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WO2018155960A1
WO2018155960A1 PCT/KR2018/002280 KR2018002280W WO2018155960A1 WO 2018155960 A1 WO2018155960 A1 WO 2018155960A1 KR 2018002280 W KR2018002280 W KR 2018002280W WO 2018155960 A1 WO2018155960 A1 WO 2018155960A1
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ovarian cancer
bacteria
derived
extracellular vesicles
decrease
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Korean (ko)
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김윤근
박태성
송용상
김세익
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주식회사 엠디헬스케어
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Priority to US16/629,360 priority Critical patent/US20200199655A1/en
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/6895—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for plants, fungi or algae

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  • the present invention relates to a method for diagnosing ovarian cancer through a microbial metagenome analysis, and more specifically, by performing a microbial metagenomic analysis of bacteria, archaea, etc., using a sample derived from a subject,
  • the present invention relates to a method for diagnosing ovarian cancer by analyzing the increase or decrease in content.
  • Ovarian cancer is the second most common cancer of the genitals. However, 70% of women are diagnosed at an advanced stage, so the treatment rate is only 20-30%. The cause of ovarian cancer, as with other cancers, is not yet known exactly. For some factors, there is a high risk of ovarian cancer if there is ovarian cancer in the family, but 95% of ovarian cancer patients have no family history. If you have a past or family history of breast cancer, endometrial cancer, or rectal cancer, breast cancer will double your chances of developing ovarian cancer. Persistent ovulation and menstruation are known to increase the risk of ovarian cancer.
  • ovarian cancer On the other hand, pregnancy tends to prevent the occurrence of ovarian cancer, so once a child is born, the risk of ovarian cancer is about 10% less than that of a woman who never gives birth. Feeding after birth also reduces the number of ovulation to reduce the occurrence of ovarian cancer. Due to environmental factors, ovarian cancers occur in advanced countries and urban women. In addition, obesity and infectivity of various viral diseases are known to be related to the occurrence of ovarian cancer.
  • Vaginal ultrasound and tumor markers are mainly used to diagnose ovarian cancer, and CA125, CA19-9, AFP, CEA, SA, and CA72.4 are used as tumor indicators.
  • the CA125 is widely used for screening, diagnostics, monitoring, and follow-up. However, the specificity and sensitivity are limited in stage 1 and 2 of ovarian cancer.
  • the symbiosis of the human body reaches 100 trillion times 10 times more than human cells, the number of genes of the microorganism is known to be more than 100 times the number of human genes.
  • a microbiota is a microbial community, including bacteria, archaea, and eukarya that exist in a given settlement.
  • the intestinal microbiota plays an important role in human physiology.
  • it is known to have a great effect on human health and disease through interaction with human cells.
  • the symbiotic bacteria secrete nanometer-sized vesicles to exchange information about genes and proteins in other cells.
  • the mucous membrane forms a physical protective film that particles larger than 200 nanometers (nm) in size can't pass through, so that the symbiotic bacteria cannot pass through the mucosa, but bacterial-derived vesicles are usually less than 100 nanometers in size. It freely speaks to the mucous membrane and is absorbed by our body.
  • Metagenomics also called environmental genomics, can be said to be an analysis of metagenomic data obtained from samples taken from the environment (Korean Patent Publication No. 2011-0073049). Recently, it has become possible to list the bacterial composition of the human microflora by a method based on 16s ribosomal RNA (16s rRNA) sequencing. Next generation sequencing of 16s rDNA sequencing gene of 16s ribosomal RNA is performed. , NGS) platform to analyze.
  • NGS 16s ribosomal RNA
  • the present inventors In order to diagnose ovarian cancer, the present inventors separated extracellular vesicles using blood and urine, a sample derived from a subject, extracted genes from vesicles, and performed a metagenome analysis on the vesicles. Bacteria and archaea-derived extracellular vesicles that can act were identified, and the present invention was completed.
  • an object of the present invention is to provide an information providing method for diagnosing ovarian cancer through metagenome analysis of extracellular vesicles derived from bacteria and archaea.
  • the present invention provides a method for providing information for diagnosing ovarian cancer, comprising the following steps:
  • the present invention provides a method for diagnosing ovarian cancer, comprising the following steps:
  • the present invention also provides a method for predicting the risk of developing ovarian cancer, comprising the following steps:
  • the subject sample may be blood or urine.
  • At least one class selected from the group consisting of Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 isolated from the subject blood sample in step (c). ) May be compared to increase or decrease the content of bacterial-derived extracellular vesicles.
  • Rhizobiaceae Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Burcobacteria coccaceae It may be to compare the increase or decrease in the content of one or more family bacteria-derived extracellular vesicles selected from the group consisting of, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae.
  • family bacteria-derived extracellular vesicles selected from the group consisting of, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae,
  • At least one phylum selected from the group consisting of Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, and TM6 isolated from the subject urine sample in step (c) It may be to compare the increase or decrease in the content of bacterial-derived extracellular vesicles.
  • Mollicutes, Deferribacteres, Fusobacteriia, Fimbriimonadia, Erysipelotrichi, Chloroplast, Gammaproteobacteria, Betaproteobacteria, Bacilli, Acidimicrobiia, Deltaproteobacteria, Oscillatoriophycidadee , Gemmatimonadetes, Flavobacteriia, ML635J-21, and SJA-4 may be compared to increase or decrease the content of one or more class bacteria-derived extracellular vesicles selected from the group consisting of.
  • the blood may be whole blood, serum, plasma, or blood monocytes.
  • Extracellular vesicles secreted by the microorganisms present in the environment can be absorbed directly into the body and directly affect the development of cancer, and ovarian cancer is difficult to diagnose early because symptoms are difficult, so effective treatment is difficult.
  • Metagenome analysis of extracellular vesicles derived from bacteria using a sample predicts the risk of developing ovarian cancer in advance, so that risk groups of ovarian cancer can be diagnosed and predicted early and appropriate management can be delayed or prevented. It can be diagnosed early, reducing the incidence of ovarian cancer and improving the therapeutic effect.
  • metagenome analysis in patients diagnosed with ovarian cancer can be used to avoid the causative agent and improve the course of the cancer or prevent recurrence.
  • Figure 1a is a photograph of the distribution of bacteria and vesicles by time after the oral administration of enteric bacteria and bacterial derived vesicles (EV) to the mouse
  • Figure 1b is 12 hours after oral administration, blood And several organs were extracted to evaluate the distribution of bacteria and vesicles in the body.
  • FIG. 2 is a result showing the distribution of bacterial vesicles (EVs) with significant diagnostic performance at the class level by separating bacterial vesicles from ovarian cancer patients and normal blood, and performing a metagenome analysis.
  • EVs bacterial vesicles
  • Figure 3 shows the distribution of bacteria-derived vesicles from ovarian cancer patients and normal blood, and shows the distribution of bacterial-derived vesicles (EVs) of significant diagnostic performance at the order level by performing a metagenome analysis.
  • EVs bacterial-derived vesicles
  • Figure 4 shows the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the family level by separating bacteria-derived vesicles from ovarian cancer patients and normal blood, and performing a metagenome analysis.
  • EVs bacteria-derived vesicles
  • FIG. 5 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the genus level after separating the bacteria-derived vesicles from ovarian cancer patients and normal blood.
  • EVs bacteria-derived vesicles
  • FIG. 6 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the phylum level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine.
  • EVs bacteria-derived vesicles
  • FIG. 7 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the class level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine, and performing a metagenome analysis.
  • EVs bacteria-derived vesicles
  • FIG. 8 shows the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the order level after separation of bacteria-derived vesicles from ovarian cancer patients and normal urine.
  • EVs bacteria-derived vesicles
  • FIG. 9 is a result showing the distribution of bacteria-derived vesicles (EVs) with significant diagnostic performance at the family level by separating bacteria-derived vesicles from ovarian cancer patients and normal urine, and performing a metagenome analysis.
  • EVs bacteria-derived vesicles
  • FIG. 10 shows the distribution of bacterial vesicles (EVs) with significant diagnostic performance at the genus level after isolation of bacterial vesicles from ovarian cancer patients and normal urine.
  • EVs bacterial vesicles
  • the present invention relates to a method for diagnosing ovarian cancer through microbial metagenome analysis, and the present inventors separated extracellular vesicles using a sample derived from a subject, extracted genes from vesicles, and performed a metagenome analysis. , Extracellular vesicles derived from bacteria that can act as a causative agent of ovarian cancer were identified.
  • the present invention comprises the steps of (a) extracting DNA from the extracellular vesicles isolated from the subject sample;
  • (C) provides an information providing method for diagnosing ovarian cancer comprising the step of comparing the increase and decrease of the content of bacteria and archaea-derived extracellular vesicles and the normal-derived sample through the sequencing of the PCR product.
  • the term "diagnosed ovarian cancer” means to determine whether a ovarian cancer is likely to develop, whether the ovarian cancer is relatively high, or whether an ovarian cancer has already occurred in a patient. .
  • the method of the present invention can be used to prevent or delay the onset of the disease through special and appropriate management as a patient at high risk of developing ovarian cancer for any particular patient.
  • the methods of the present invention can be used clinically to determine treatment by early diagnosis of ovarian cancer and selecting the most appropriate treatment regimen.
  • metagenome used in the present invention, also referred to as “metagenome”, refers to the total of the genome including all viruses, bacteria, fungi, etc. in an isolated area such as soil, animal intestine, It is mainly used as a concept of genome explaining the identification of many microorganisms at once using sequencer to analyze microorganisms which are not cultured.
  • metagenome does not refer to one species of genome or genome, but refers to a kind of mixed dielectric as the genome of all species of one environmental unit. This is a term from the point of view of defining a species in the course of the evolution of biology in terms of functional species as well as various species that interact with each other to create a complete species.
  • metagenome analysis was preferably performed using bacteria-derived extracellular vesicles isolated from blood and urine.
  • bacterial vesicle is a concept including not only bacteria but also extracellular vesicles secreted by archaea, but is not limited thereto.
  • the subject sample may be blood or urine, and the blood may preferably be whole blood, serum, plasma, or blood monocytes, but is not limited thereto.
  • the metagenome analysis of the extracellular vesicles derived from bacteria and archaea was performed, and at the level of phylum, class, order, family, and genus, Each analysis identified bacterial vesicles that could actually cause ovarian cancer.
  • the analysis of the bacterial metagenome at the level of the vesicles present in the blood samples from the subject Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 strong bacteria
  • the bacterial metagenome was analyzed at the neck level for vesicles present in a blood sample derived from a subject, Erysipelotrichales, Rhizobiales, Caulobacterales, Pseudomonadales, Coriobacteriales, Flavobacteriales, YS2, Chroococcales, CW040, Desulfovibrionales, and Methylophilales Neck bacteria-derived extracellular vesicles were significantly different between ovarian cancer patients and normal subjects (see Example 4).
  • Rhizobiaceae as a result of analyzing the bacterial metagenome at the level of the vesicles present in the blood samples from the subject, Rhizobiaceae, Bradyrhizobiaceae, Peptostreptococcaceae, Oxalobacteraceae, Erysipelotrichaceae, Pseudomonadaceae, Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae Fusobacteriaceae, Planococcaceae, Burkholderiaceae, Aerococcaceae, Lactobacillaceae, Coriobacteriaceae, Weeksellaceae, Xenococcaceae, F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae were significantly different between ovarian cancer patients and normal individuals. (See Example 4).
  • the present invention as a result of analyzing the bacterial metagenome at the genus level for the vesicles present in the blood samples from the subject, Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, The contents of extracellular vesicles derived from bacteria belonging to Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, and Collinsella were significantly different between ovarian cancer patients and normal individuals (see Example 4).
  • the bacterial metagenome of the vesicles present in the subject-derived urine sample at the gate level Tenericutes, Deferribacteres, Fusobacteria, Armatimonadetes, SR1, Gemmatimonadetes, TM6 door bacteria-derived cells
  • the bacterial metagenome was analyzed at the neck level for vesicles present in the urine sample derived from the subject, Desulfuromonadales, Desulfobacterales, Gallionellales, Cardiobacteriales, Stramenopiles, Marinicellales, Halanaerobiales, RF39, Deferribacterales, Pirellulales, Fusobacteriales, Fimbriimonadales, Erysipelotrichales, Pseudomonadales, Streptophyta, Turicibacterales, Burkholderiales, Sphingomonadales, Myxococcales, Thermales, YS2, Bacillales, Acidimicrobiales, Oceanospirillales, Legionellales, iiiccales Rhocolate Flaco, iii1-15 The contents of MLE1-12, Methylophilales, and Ellin6067 throat bacterial extracellular vesicles were significantly different between ovarian cancer patients and normal
  • the bacterial metagenome of the vesicles present in the subject-derived urine sample at the excess level Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaceae, Pseudonocardiaceae, Leuconostocaceae, Moraxellaceae, Methylobacteriaceae, Paraprevotellaceae, Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclfoaceae, Saceae, Deaceae Was significantly different between ovarian
  • the bacterial metagenome of the vesicles present in the subject-derived urine sample at the genus level Morganella, Rhizobium, Exiguobacterium, Cupriavidus, Ralstonia, Cellulomonas, Sporosarcina, Proteus, Leptotrichia, SMB53, Prevotella, Oribacterium, Pediococcus, Paraprevotella, Methylobacterium, Mucispirillum, Catenibacterium, Parabacteroides, Collinsella, Anaerostipes, Pseudomonas, Butyricimonas, Fusobacterium, Weissella, Eubacterium, Dialister, Actinomyoccephactus Docebacci Ocedociocce phactus Erwinia, Staphylococcus, Citrobacter, Halomonas, Sphingobium, Gordonia, Adlercreutzia, Bre
  • the present invention through the results of the above embodiment, by identifying the bacteria-derived extracellular vesicles isolated from blood and urine by metagenomic analysis of bacteria-derived vesicles significantly changed in ovarian cancer patients compared to normal people Meta-genomic analysis confirmed that ovarian cancer can be diagnosed by analyzing the increase and decrease of the contents of the bacteria-derived vesicles at each level.
  • the fluorescently labeled 50 ⁇ g of bacteria and bacteria-derived vesicles were administered in the same manner as above 12 hours.
  • Blood, Heart, Lung, Liver, Kidney, Spleen, Adipose tissue, and Muscle were extracted from mice.
  • the intestinal bacteria (Bacteria) were not absorbed into each organ, whereas the intestinal bacteria-derived extracellular vesicles (EV) were detected in the tissues, as shown in FIG. And distribution in liver, kidney, spleen, adipose tissue, and muscle.
  • PCR was performed using the 16S rDNA primer shown in Table 1 to amplify the gene and perform sequencing (Illumina MiSeq sequencer). Output the result as a Standard Flowgram Format (SFF) file, convert the SFF file into a sequence file (.fasta) and a nucleotide quality score file using GS FLX software (v2.9), check the credit rating of the lead, and window (20 bps) The part with the average base call accuracy of less than 99% (Phred score ⁇ 20) was removed.
  • SFF Standard Flowgram Format
  • the Operational Taxonomy Unit performed UCLUST and USEARCH for clustering according to sequence similarity. Specifically, the clustering is based on 94% genus, 90% family, 85% order, 80% class, and 75% sequence similarity. OTU's door, river, neck, family and genus level classifications were performed, and bacteria with greater than 97% sequence similarity were analyzed using BLASTN and GreenGenes' 16S DNA sequence database (108,453 sequences) (QIIME).
  • Example 3 By the method of Example 3, the vesicles were isolated from the blood of 137 patients with ovarian cancer and 139 normal-matched age and sex, followed by metagenome sequencing. In the development of the diagnostic model, the strains whose p-value between the two groups is 0.05 or less and more than two times different between the two groups are selected in the t-test. under curve), sensitivity, and specificity.
  • vesicle-derived vesicles in the blood at the class level revealed a diagnostic model for ovarian cancer when a diagnostic model was developed using Erysipelotrichi, Alphaproteobacteria, Coriobacteriia, Flavobacteriia, Oscillatoriophycideae, Deltaproteobacteria, and ML635J-21 strong bacterial biomarkers. Performance was significant (see Table 2 and FIG. 2).
  • Control Ovarian Cancer Training Testing name Mean SD Mean SD p value Ratio AUC sensitivity specificity AUC sensitivity specificity o__Erysipelotrichales 0.0094 0.0136 0.0019 0.0026 0.0000 0.20 0.69 0.81 0.54 0.67 0.79 0.54 o__Rhizobiales 0.0268 0.0319 0.0060 0.0052 0.0000 0.22 0.79 0.89 0.62 0.78 0.88 0.62 o__Caulobacterales 0.0064 0.0109 0.0017 0.0027 0.0000 0.27 0.62 0.80 0.42 0.60 0.80 0.41 o__Pseudomonadales 0.1657 0.1328 0.0647 0.0386 0.0000 0.39 0.79 0.84 0.60 0.78 0.83 0.60 o__Coriobacteriales 0.0064 0.0086 0.0183 0.0111 0.0000 2.84 0.83 0.74 0.79 0.82 0.74 0.79 o__Flavobacteriales 0.0057 0.0121 0.0177 0.0226 0.0000
  • Rhizobiaceae Rhizobiaceae
  • Bradyrhizobiaceae Peptostreptococcaceae
  • Oxalobacteraceae Erysipelotrichaceae
  • Pseudomonadaceae Caulobacteraceae, Methylobacteriaceae, Paraprevotellaceae, Fusobacteriaceae, Planococcaceae, Burkccaceae, Cocoaceae, Burkholderaceae Cocoaceae
  • diagnostic models were developed with F16, Desulfovibrionaceae, Comamonadaceae, S24-7, and Methylophilaceae and bacterial biomarkers
  • diagnostic performance for ovarian cancer was significant (see Table 4 and FIG. 4).
  • Bacterial-derived vesicles in the blood were analyzed at the genus level and found to be Morganella, Hydrogenophilus, Cupriavidus, Eubacterium, Catenibacterium, Micrococcus, Coprococcus, Pseudomonas, Paraprevotella, Sphingomonas, Faecalibacterium, Blautia, Serratia, Citrobacter, and Collinsella genus
  • the diagnostic performance for ovarian cancer was significant (see Table 5 and FIG. 5).
  • Control Ovarian Cancer Training Testing Mean SD Mean SD p value Ratio AUC sensitivity specificity AUC sensitivity specificity g__Morganella 0.0012 0.0032 0.0000 0.0002 0.0000 0.02 0.66 0.95 0.29 0.64 0.94 0.28 g__Hydrogenophilus 0.0012 0.0048 0.0000 0.0003 0.0042 0.04 0.62 0.89 0.27 0.58 0.86 0.27 g__Cupriavidus 0.0242 0.0432 0.0011 0.0017 0.0000 0.04 0.75 0.90 0.55 0.73 0.90 0.55 g __ [Eubacterium] 0.0026 0.0055 0.0003 0.0008 0.0000 0.11 0.68 0.86 0.41 0.65 0.86 0.40 g__Catenibacterium 0.0050 0.0105 0.0007 0.0015 0.0000 0.13 0.68 0.82 0.44 0.68 0.81 0.44 g___Micrococcus 0.0115 0.0185 0.0019 0.0034 0.0000 0.17 0.66 0.84 0.44 0.
  • Example 3 By the method of Example 3, the vesicles were isolated from the urine of 135 patients with ovarian cancer and 136 normal people with age and sex matched with metagenome sequencing. In the development of the diagnostic model, the strains whose p-value between the two groups is 0.05 or less and more than two times different between the two groups are selected in the t-test. under curve), sensitivity, and specificity.
  • Cardiobacteriaceae Analysis of bacteria-derived vesicles in urine at the family level revealed Cardiobacteriaceae, Acidobacteriaceae, Oxalobacteraceae, Prevotellaceae, Leptotrichiaceae, Christensenellaceae, Barnesiellaceae, Fimbriimonadaceae, Erysipelotrichaceae, Mogibacteriaceae, Pseudomonadaceae, Fusobacteriaocae, Pseeu When developing diagnostic models with Sphingomonadaceae, Nocardioidaceae, Lactobacillaceae, Burkholderiaceae, Aerococcaceae, Nocardiopsaceae, Rhodocyclaceae, S24-7, Eubacteriaceae, Desulfovibrionaceae, Comamonadaceae, Methylophilaceae, and Bacterial Biomarkers, Significant Diagnostic Performance for Ovarian Cancer (See Table 9 and FIG. 9).
  • Control Ovarian Cancer Training Testing Mean SD Mean SD p value Ratio AUC sensitivity specificity AUC sensitivity specificity g__Morganella 0.0091 0.0243 0.0000 0.0002 0.0000 0.00 0.80 0.98 0.54 0.79 0.98 0.53 g__Rhizobium 0.0034 0.0036 0.0000 0.0001 0.0000 0.00 0.96 0.99 0.88 0.95 0.99 0.88 g___Exiguobacterium 0.0017 0.0077 0.0000 0.0001 0.0134 0.01 0.70 0.92 0.43 0.70 0.89 0.43 g__Cupriavidus 0.0333 0.0988 0.0016 0.0028 0.0002 0.05 0.73 0.78 0.58 0.73 0.77 0.56 g__Ralstonia 0.0165 0.0483 0.0012 0.0026 0.0003 0.07 0.70 0.75 0.54 0.69 0.73 0.53 g___Cellulomonas 0.0007 0.0017 0.0001 0.0003 0.0000 0.08 0.67 0.80 0.43 0.67
  • the method for providing information on the diagnosis of ovarian cancer through the bacterial metagenomic analysis according to the present invention is carried out by performing a bacterial metagenomic analysis using a sample derived from a subject to analyze the increase or decrease in the content of specific bacterial-derived extracellular vesicles. It can be used to predict risk and diagnose ovarian cancer.
  • Extracellular vesicles secreted by the bacteria present in the environment can be absorbed directly into the body and directly affect the development of cancer, and ovarian cancer is difficult to diagnose effectively because of the early diagnosis of symptoms before the symptoms appear, the human-derived according to the present invention Metagenome analysis of bacterial-derived extracellular vesicles using a sample predicts the risk of developing ovarian cancer in advance, allowing early diagnosis and prediction of risk groups for ovarian cancer, and delaying the onset or preventing the onset through proper management. Early diagnosis is possible even after the onset of cancer, which can lower the incidence of ovarian cancer and increase the therapeutic effect.
  • the bacterial metagenomic analysis according to the present invention in patients diagnosed with ovarian cancer can be used to improve the progression of ovarian cancer or to prevent recurrence by avoiding causal agent exposure.

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Abstract

La présente invention concerne un procédé de diagnostic du cancer de l'ovaire par l'analyse du métagénome microbien et, plus particulièrement, concerne un procédé permettant effectuer une analyse métagénomique à l'aide d'un échantillon issu d'un sujet de façon à analyser les augmentations et les diminutions de la quantité de vésicules extracellulaires issues de bactéries ou d'archaea spécifiques, ce qui permet de diagnostiquer le cancer de l'ovaire. Les vésicules extracellulaires sécrétées par les bactéries présentes dans l'environnement sont absorbées dans le corps de façon à influencer directement l'apparition d'un cancer, et étant donné que le diagnostic précoce du cancer de l'ovaire avant que les symptômes ne se produisent est difficile, un traitement efficace a été difficile. Par l'intermédiaire de l'analyse du métagénome à l'aide d'un échantillon d'origine humaine, conformément à la présente invention, le risque d'apparition d'un cancer de l'ovaire peut être prédit à l'avance de telle sorte que des groupes à risque de cancer de l'ovaire sont diagnostiqués et prédit à un stade précoce, ce qui permet de retarder le moment de l'apparition de la maladie ou de retarder l'apparition de la maladie à prévenir par l'intermédiaire d'une gestion appropriée, et le diagnostic précoce est possible même après l'apparition de la maladie, ce qui permet d'abaisser l'incidence du cancer de l'ovaire et d'augmenter les effets thérapeutiques.
PCT/KR2018/002280 2017-02-24 2018-02-23 Procédé de diagnostic du cancer de l'ovaire par analyse du métagénome microbien WO2018155960A1 (fr)

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Cited By (7)

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US20200199655A1 (en) * 2017-02-24 2020-06-25 Md Healthcare Inc. Method for diagnosing ovarian cancer through microbial metagenome analysis
CN111500732A (zh) * 2020-05-25 2020-08-07 福建医科大学 微生物在作为子宫内膜癌的诊断标志物中的应用和试剂盒
CN113194970A (zh) * 2018-12-10 2021-07-30 Md保健株式会社 来源于魏斯氏菌属细菌的纳米囊泡及其用途
EP3739067A4 (fr) * 2018-01-12 2021-10-13 MD Healthcare Inc. Nanovésicules issues de bactéries morganella et utilisations associées
EP3760742A4 (fr) * 2018-02-28 2021-12-01 MD Healthcare Inc. Nanovésicules issues de bactéries micrococcus et leur utilisation
EP3763829A4 (fr) * 2018-03-05 2021-12-01 MD Healthcare Inc. Nanovésicules issues de bactéries enhydrobacter, et leur utilisation
EP3760743A4 (fr) * 2018-02-28 2021-12-01 MD Healthcare Inc. Nanovésicules dérivées de bactéries rhizobium sp., et leur utilisation

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US20200199655A1 (en) * 2017-02-24 2020-06-25 Md Healthcare Inc. Method for diagnosing ovarian cancer through microbial metagenome analysis
EP3739067A4 (fr) * 2018-01-12 2021-10-13 MD Healthcare Inc. Nanovésicules issues de bactéries morganella et utilisations associées
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EP3760742A4 (fr) * 2018-02-28 2021-12-01 MD Healthcare Inc. Nanovésicules issues de bactéries micrococcus et leur utilisation
EP3760743A4 (fr) * 2018-02-28 2021-12-01 MD Healthcare Inc. Nanovésicules dérivées de bactéries rhizobium sp., et leur utilisation
EP3763829A4 (fr) * 2018-03-05 2021-12-01 MD Healthcare Inc. Nanovésicules issues de bactéries enhydrobacter, et leur utilisation
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CN113194970A (zh) * 2018-12-10 2021-07-30 Md保健株式会社 来源于魏斯氏菌属细菌的纳米囊泡及其用途
CN111500732A (zh) * 2020-05-25 2020-08-07 福建医科大学 微生物在作为子宫内膜癌的诊断标志物中的应用和试剂盒

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