CN109266765A - Microbial flora and application for oral precancerous lesion risk profile - Google Patents

Microbial flora and application for oral precancerous lesion risk profile Download PDF

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CN109266765A
CN109266765A CN201811136938.0A CN201811136938A CN109266765A CN 109266765 A CN109266765 A CN 109266765A CN 201811136938 A CN201811136938 A CN 201811136938A CN 109266765 A CN109266765 A CN 109266765A
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flora
precancerous lesion
oral precancerous
oral
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CN109266765B (en
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宋卓
卢秦
孙海鹏
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Human And Future Biotechnology (changsha) Co Ltd
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    • 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
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Abstract

The invention discloses for oral precancerous lesion risk profile micropopulation and application, the analysis of 127 normal healthy controls of total based on measurement of inventor, 124 oral precancerous lesion fecal sample sequencing datas identify classification of flora marker therein.Based on micropopulation of the invention, oral precancerous lesion risk can be effectively predicted.Based on the diagnostic reagent of micropopulation of the present invention exploitation, oral precancerous lesion risk can be effectively predicted, detection process is noninvasive, highly-safe.Simultaneously as flora sample can be transported at room temperature, the convenience of detection is substantially increased.

Description

Microbial flora and application for oral precancerous lesion risk profile
Technical field
The present invention relates to the microbial floras and application for oral precancerous lesion risk profile.
Background technique
Carcinoma of mouth is the most common head-neck malignant tumor, and in recent years, the disease incidence of carcinoma of mouth has rising in many countries Trend.Carcinoma of mouth can be developed by oral precancerous lesions such as oral leukoplakias, and oral precancerous lesion is that one is with mucous membrane of mouth Precancerous lesion with the characteristics of the change of the mucous membrane of mouth such as lower fibrosis and lichen planus of mouth, precancerous lesion have the possibility of very big canceration Property, because lacking the tumor biomarker of early screening and detection, late the stage could by 50% or more oral cancer patient It is made a definite diagnosis, the tumor markers that discovery can be used for carcinoma of mouth early diagnosis just become research hotspot.
Jiang Jingang, present Research [J] clinic medical officer's magazine of the tight ball oral precancerous lesion marker that shakes, 2006,34 (2): point out that the marker of research oral precancerous lesion is of great significance to the chemoprophylaxis of mouth neoplasm in 224-227., but There is presently no single indexs can obtain promising result, and people are also constantly looking for not only having substantial connection with cancer, but also Carcinogenesis occur high specificity, high sensitivity Biological indicators as canceration biomarker.Research at present and application Index have: (1) marker relevant to cell Proliferation, such as proliferating cell nuclear antigen, argyrophilic nucleolar organi-zer regions, Ki67/ Mib-1, Telomerase etc..(2) marker relevant to Apoptosis, such as Bcl-2/Bax, Survivin, epoxide hydrolase 2.(3) Specific gene, such as p53 gene and its expression product.(4) chromosome aberration, as Microsatellite marker and loss of heterozygosity, DNA contain Amount etc..
Application [J] mouthful of Yu Xuedi, Sun Hong the English metabolism group in carcinoma of mouth and the research of oral precancerous lesion marker 2017,37 (04): chamber medicine points out that the analysis method based on metabonomic technology has discovery tumour correlation mark in 369-372. The research of the potential of will object, more and more risk Pre-Evaluation for being used for major disease and early diagnosis etc..State in recent years Inside and outside many scholars explore the abnormal metabolism change of disease before carcinoma of mouth and carcinoma of mouth using metabonomic technology, to the saliva of patient The metabolite of liquid, blood, urine, tissue etc. is studied, to find disease associated biomarkers, for clinical early stage Diagnosis and treatment provide objective evidence.But the metabolin research based on omics technology still has problem, every kind of experiment sample This, every kind of analytical technology has corresponding limitation.Current research is concentrated mainly on cancer field, oral precancerous lesion field Study deep not enough, it is very few that the metabolism that oral precancerous lesion is developed to carcinoma of mouth this process changes research.In addition, carcinoma of mouth And the occurrence and development of precancerous lesion are that multiple affair is coefficient as a result, the metabolin research for only relying on single level is not sufficient to Comprehensively analyzed.Numerous studies have obtained potential marker relevant to the variation of disease physiological and pathological, but each marker Between relevance it is not strong.And the research for finding new tumor related marker object at present is more, few relevant clinical verifications Report.These problems also need us to focus on conducting further research and verifying with integrity ideology.
Mager D L,Haffajee AD,Devlin P M,et al.The salivary microbiota as a diagnostic indicator of oral cancer:a descriptive,non-randomized study of cancer-free and oral squamous cell carcinoma subjects[J].Journal of 2005,3 (1): translational medicine is had detected 40 in oral cancer patient's saliva in 27. using DNA hybridization technology Kind common bacteria, discovery porphyromonas gingivalis, prevotella melanogenicus and the horizontal significant up-regulation of light chain coccus, to carcinoma of mouth Early screening has certain help.
How the prior art is simultaneously not known by predicting oral precancerous lesion risk the case where microbial flora.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide one group can efficiently predict oral precancerous lesion wind The micropopulation of danger.
It is another object of the present invention to provide the reagents of Quantitative microbial group's abundance to prepare oral precancerous lesion wind Application in danger prediction reagent.
The technical solution used in the present invention is:
Before a kind of microbial flora for oral precancerous lesion risk profile, including Healthy People enrichment flora and carcinoma of mouth Lesion patient is enriched with flora,
Healthy People enrichment flora at least contains:
p__Firmicutes|c__Bacilli|o__Lactobacillales、f__Prevotellaceae|g__ Prevotella|s__nanceiensis、
f__Veillonellaceae|g__Megasphaera、c__TM7-3|o__CW040、
o__Clostridiales|f__Peptostreptococcaceae、o__Rickettsiales|f__ mitochondria、
f__Tissierellaceae|g__WAL_1855D、f__Staphylococcaceae|g__ Staphylococcus、
f__Tissierellaceae|g__ph2、o__Campylobacterales|f__Helicobacteraceae、
g__Paenibacillus|s__lautus；
Oral precancerous lesion patient is enriched with flora and at least contains:
f__Micrococcaceae|g__Rothia|s__dentocariosa、f__Enterococcaceae|g__ Vagococcus、
f__Porphyromonadaceae|g__Paludibacter、
f__Flavobacteriaceae|g__Capnocytophaga|s__ochracea、
f__Prevotellaceae|g__Prevotella|s__stercorea、f__Halomonadaceae|g__ Halomonas、
c__Bacilli|o__Gemellales、f__Veillonellaceae|g__Acidaminococcus、
f__Bacteroidaceae|g__Bacteroides|s__coprophilus、o__Aeromonadales|f__ Aeromonadaceae、
f__Clostridiaceae|g__Clostridium、f__Micrococcaceae|g__Micrococcus、
f__Erysipelotrichaceae|g__Bulleidia|s__moorei、
f__Helicobacteraceae|g__Helicobacter|s__pylori、f__Erysipelotrichaceae |g__Holdemania、
f__Enterobacteriaceae|g__Serratia、c__Clostridia|o__SHA-98、
f__Pasteurellaceae|g__Actinobacillus、f__Pseudomonadaceae|g__Azomonas、
g__Ruminococcus|s__torques、o__Oceanospirillales|f__Halomonadaceae、
f__Erysipelotrichaceae|g__Coprobacillus。
As the further improvement of mentioned microorganism group, oral precancerous lesion risk is calculated by flora abundance, meter It is as follows to calculate formula:
In formula, IjFor risk index, AijIt is the relative abundance of strain i in sample j, N is selected where being in Healthy People flora The subset of middle enrichment flora, M is the subset with oral precancerous lesion relative enrichment flora, | N | with | M | be the first subset respectively with The number of strain described in second subset.
A kind of kit for oral precancerous lesion risk profile, the reagent containing quantitative mentioned microorganism group.
As the further improvement of mentioned reagent box, oral precancerous lesion risk is calculated by flora abundance, is calculated Formula is as follows:
In formula, IjFor risk index, AijIt is the relative abundance of strain i in sample j, N is selected where being in Healthy People flora The subset of middle enrichment flora, M is the subset with oral precancerous lesion relative enrichment flora, | N | with | M | be the first subset respectively with The number of strain described in second subset.
As the further improvement of mentioned reagent box, | N |=11.
As the further improvement of mentioned reagent box, | M |=22.
As the further improvement of mentioned reagent box, IjCritical value are as follows: -1.834078~-2.133096.
As the further improvement of mentioned reagent box, kit is PCR kit, the OTU sequence such as SEQ ID of amplification Shown in NO:1~SEQ ID NO:33.
The reagent of Quantitative microbial group's abundance is preparing the application in oral precancerous lesion risk profile reagent, micropopulation For above-mentioned micropopulation.
As the further improvement of above-mentioned application, the reagent of Quantitative microbial group's abundance is PCR reagent, the OTU of amplification Sequence is as shown in SEQ ID NO:1~SEQ ID NO:33.
The beneficial effects of the present invention are:
Based on micropopulation of the invention, oral precancerous lesion risk can be effectively predicted.
Based on the diagnostic reagent of micropopulation of the present invention exploitation, oral precancerous lesion risk can be effectively predicted, detected Journey is noninvasive, highly-safe.Simultaneously as flora sample can be transported at room temperature, the convenience of detection is substantially increased.
Detailed description of the invention
Fig. 1 is distribution situation of the microbial bacteria in crowd；
Fig. 2 is Receiver Operating Characteristics (ROC) curve of risk index；
Fig. 3 is distribution situation of the risk index in oral precancerous lesion and Healthy People；
Fig. 4 is the ROC curve of training set；
Fig. 5 is the ROC curve of test set.
Specific embodiment
127 normal healthy controls of total based on measurement of inventor, 124 oral precancerous lesion saliva sample sequencing datas Analysis, identifies classification of flora marker therein, distribution situation of the microbial bacteria in crowd is as shown in Figure 1.It is specific as follows:
Healthy People enrichment flora contains: p__Firmicutes | c__Bacilli | o__Lactobacillales,
f__Prevotellaceae|g__Prevotella|s__nanceiensis、f__Veillonellaceae|g__ Megasphaera、
c__TM7-3|o__CW040、o__Clostridiales|f__Peptostreptococcaceae、
o__Rickettsiales|f__mitochondria、f__Tissierellaceae|g__WAL_1855D、
f__Staphylococcaceae|g__Staphylococcus、f__Tissierellaceae|g__ph2、
o__Campylobacterales|f__Helicobacteraceae,g__Paenibacillus|s__lautus；
Oral precancerous lesion patient is enriched with flora and contains: f__Micrococcaceae | g__Rothia | s__ dentocariosa、
f__Enterococcaceae|g__Vagococcus、f__Porphyromonadaceae|g__ Paludibacter、
f__Flavobacteriaceae|g__Capnocytophaga|s__ochracea、
f__Prevotellaceae|g__Prevotella|s__stercorea、f__Halomonadaceae|g__ Halomonas、
c__Bacilli|o__Gemellales、f__Veillonellaceae|g__Acidaminococcus、
f__Bacteroidaceae|g__Bacteroides|s__coprophilus、o__Aeromonadales|f__ Aeromonadaceae、
f__Clostridiaceae|g__Clostridium、f__Micrococcaceae|g__Micrococcus、
f__Erysipelotrichaceae|g__Bulleidia|s__moorei、f__Helicobacteraceae| g__Helicobacter|s__pylori、
f__Erysipelotrichaceae|g__Holdemania、f__Enterobacteriaceae|g__ Serratia、
c__Clostridia|o__SHA-98、f__Pasteurellaceae|g__Actinobacillus、
f__Pseudomonadaceae|g__Azomonas、g__Ruminococcus|s__torques、
o__Oceanospirillales|f__Halomonadaceae、f__Erysipelotrichaceae|g__ Coprobacillus。
Table 1 is enrichment condition of each species of microbial bacteria in crowd:
The enrichment condition of table 1, different microorganisms in crowd
Bacterium name Oral precancerous lesion Health Enrichment crowd
p__Firmicutes|c__Bacilli|o__Lactobacillales 0.010573 0.012844 Health
f__Micrococcaceae|g__Rothia|s__dentocariosa 0.007655 0.004857 Oral precancerous lesion
f__Prevotellaceae|g__Prevotella|s__nanceiensis 0.004843 0.007299 Health
f__Veillonellaceae|g__Megasphaera 0.006193 0.007772 Health
c__TM7-3|o__CW040 0.001799 0.003649 Health
f__Enterococcaceae|g__Vagococcus 0.000281 0.000197 Oral precancerous lesion
f__Porphyromonadaceae|g__Paludibacter 0.00103 0.000533 Oral precancerous lesion
o__Clostridiales|f__Peptostreptococcaceae 0.000405 0.000616 Health
f__Flavobacteriaceae|g__Capnocytophaga|s__ochracea 0.00091 0.000477 Oral precancerous lesion
f__Prevotellaceae|g__Prevotella|s__stercorea 0.000613 6.48E-06 Oral precancerous lesion
f__Halomonadaceae|g__Halomonas 0.000187 0.000112 Oral precancerous lesion
c__Bacilli|o__Gemellales 9.33E-05 6.40E-05 Oral precancerous lesion
f__Veillonellaceae|g__Acidaminococcus 0.000338 6.50E-05 Oral precancerous lesion
o__Rickettsiales|f__mitochondria 0.000298 0.000614 Health
f__Bacteroidaceae|g__Bacteroides|s__coprophilus 9.01E-05 6.47E-07 Oral precancerous lesion
o__Aeromonadales|f__Aeromonadaceae 0.000286 2.48E-05 Oral precancerous lesion
f__Tissierellaceae|g__WAL_1855D 0 1.30E-05 Health
f__Staphylococcaceae|g__Staphylococcus 1.92E-05 3.77E-05 Health
f__Clostridiaceae|g__Clostridium 7.52E-05 6.06E-06 Oral precancerous lesion
f__Tissierellaceae|g__ph2 3.04E-06 1.77E-05 Health
f__Micrococcaceae|g__Micrococcus 1.05E-05 2.83E-06 Oral precancerous lesion
f__Erysipelotrichaceae|g__Bulleidia|s__moorei 2.98E-05 1.36E-05 Oral precancerous lesion
f__Helicobacteraceae|g__Helicobacter|s__pylori 8.00E-06 0 Oral precancerous lesion
o__Campylobacterales|f__Helicobacteraceae 7.76E-06 2.30E-05 Health
f__Erysipelotrichaceae|g__Holdemania 4.94E-06 2.94E-07 Oral precancerous lesion
f__Enterobacteriaceae|g__Serratia 2.26E-06 4.89E-07 Oral precancerous lesion
c__Clostridia|o__SHA-98 6.07E-06 0 Oral precancerous lesion
f__Pasteurellaceae|g__Actinobacillus 6.40E-06 1.02E-06 Oral precancerous lesion
f__Pseudomonadaceae|g__Azomonas 8.03E-07 5.90E-08 Oral precancerous lesion
g__Paenibacillus|s__lautus 0 2.21E-06 Health
g__Ruminococcus|s__torques 7.40E-07 0 Oral precancerous lesion
o__Oceanospirillales|f__Halomonadaceae 1.37E-06 0 Oral precancerous lesion
f__Erysipelotrichaceae|g__Coprobacillus 1.77E-06 1.62E-07 Oral precancerous lesion
Table 2 is the corresponding OTU sequence situation of different microorganisms:
The corresponding OTU sequence situation of table 2, different microorganisms
Risk index
In order to develop and use the potentiality that micropopulation carries out disease identification, inventor is developed based on determining gene marker Classification of diseases system.In order to intuitively be assessed using these saliva microbial gene markers risk, invent People calculates risk index.
Risk index IjCalculation formula are as follows:
Wherein AijIt is the relative abundance of strain i in sample j, N selects the son that flora is enriched in Healthy People flora where being Collection, and M is the subset with constipation relative enrichment flora.| N | with | M | it is of strain described in the first subset and second subset respectively Number.Wherein | N | it is 11, | M | it is 22.
Receiver Operating Characteristics (ROC) analysis of risk index is as shown in Figure 2.It can be seen that threshold value be -1.834078~- 2.133096 when classification it is preferable.
Table 3 is the risk index of specific sample.
The risk index table of table 3, specific sample
Risk index is as shown in Figure 3 in the distribution of oral precancerous lesion crowd and healthy population.It can be seen from the figure that wind Dangerous index is dramatically different in oral precancerous lesion crowd and the distribution in healthy population, has good separating capacity.
In order to verify the potential ability for carrying out oral precancerous lesion classifier using microbial flora, it is based on public database Data mining goes out the classification of diseases system based on 33 gene markers, which is selected as most by the Lasso Return Law Good gene set.
The compliance test result of classifier:
Data source: data are that the Healthy People collected early period and oral precancerous lesion person excrement carry out 16S V3-V4 sequencing It obtains, includes removal primer by the pre-treatment that Usearch carries out data, remove low quality reads, OTU cluster etc., use QIIME carries out OTU comparison.
Influence the factor analysis of microbial profile
Influence of the inventor using displacement multivariate analysis of variance (PERMANOVA) method assessment different characteristic to flora, this A little features include age, gender, OSCC state.Inventor is using in R software (https: //www.r-project.org/) " Vegan " kit is analyzed, and is replaced by 10000 times, and displacement P value is obtained.Inventor also utilizes " p.adjust " in R Kit is corrected multiple check, obtains the p value of each species using FDR method.Replace multivariate analysis of variance result such as Shown in table 4, disease has a significant impact the distribution of flora entirety as the result is shown, and age-sex does not show the distribution of flora entirety Writing influences.
Freedom degree Sum Of Sqs R2 F Pr(>F) p.adjust
Disease 1 0.295584 0.015077 3.795445 0.0003 0.0009
Gender 1 0.080932 0.004128 1.039212 0.385461 0.578192
Age 55 4.197659 0.214114 0.98 0.610239 0.610239
Using the abundance spectrum of training queue, Lasso model (R glmnet 2.0-16) is trained, species mark is selected Will object, and tested in a test set, and calculate prediction error.
It is predicted using Lasso model (R glmnet 2.0-16), inputs as morbid state, species abundance, be divided into instruction Practice collection and test set.Inventor is classified using the glmnet packet in R software to construct, and predicts to survey using anticipation function Examination collection, output is prediction result (disease probability: default threshold value is 0.5, thinks that the subject is constipation greater than 0.5). Training set and test set are divided according to the ratio of 7:3, wherein training set 176, test set is 75.
Based on micropopulation of the invention, after carrying out Lasso and returning calculating, to (176) drafting ROC curves of training set (Fig. 4), training set AUC area under the curve are 0.922.The ROC curve of test set (75) as shown in figure 5, test set in totality AUC under area be 0.815.
It can know from figure, either training set or test set, return what select variable was established using Lasso The discrimination of model is better.
SEQUENCE LISTING
<110>people and scientific and technological (Changsha) Co., Ltd of future biological
<120>microbial flora and application of oral precancerous lesion risk profile are used for
<130>
<160> 33
<170> PatentIn version 3.5
<210> 1
<211> 253
<212> DNA
<213> Lactobacillales
<400> 1
tcctgtttgc tacccacgct ttcgagcctc agcgtcagtt acagaccaga gagtcgcctt 60
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cctcttctgc actcaagttc cccagtttcc aatgaccctc cccggttgag ccgggggctt 180
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<210> 2
<211> 254
<212> DNA
<213> dentocariosa
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cccctactgc actctagtta gcccgtaccc actgcaaaac cagggttaag ccccagcctt 180
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<210> 3
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<210> 4
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<210> 5
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<213> CW040
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cctcttctgc tctcgagttc aacagttcga ataatagtct gaatggttga gccaccagat 180
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<210> 6
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cctcttctgc actcaagttc cccagtttcc aatgaccctc cccggttgag ccgggggctt 180
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<210> 7
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<400> 7
tacggaggat gcaagcgtta tccggattta ttgggtttaa agggtgcgta ggcggtatta 60
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cttcacaacc attcaagacc agcagtttca aaggcagttg cttagttgag ctaagcgctt 180
tcacctctga cttaccagcc cgccgacaga ccctttaaac ccaatgattc cggataacgc 240
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tcctgttcga tacccacgct ttcgagcttc agcgtcagtt gcgctacagc aggctgcctt 60
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cttcctgcgc actcaagtct ggcagttcgc gctgcaatgc ccaggttgag ccccgacatt 180
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<210> 11
<211> 253
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<213> Halomonas
<400> 11
tcctgtttgc tacccacgct ttcgcacctc agtgtcagtg tcagtccaga aggccgcctt 60
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cctctcctgc actctagcct gacagttccg gatgccgttc ccaggttgag cccggggctt 180
tcacaaccgg cttatcaagc cacccacgcg cgctttacgc ccagtacttc ccattaacgc 240
tagcaccctc cgt 253
<210> 12
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<213> Gemellales
<400> 12
tcccgttcgc tcccctggct ttcgcgcctc agcgtcagtt ttcgtccaga aagtcgcctt 60
cgccactggt gttcctccta atctctacgc atttcaccgc tacactagga attccacttt 120
tctctcctgc actcaagttt aacagtttcc aatgaccctc cacggttgag ccgtgggctt 180
tcacatcaga cttgttaaac cacctgcgca ccctttacgc ccagttattc cggataacgc 240
ttgcccccta cgta 254
<210> 13
<211> 252
<212> DNA
<213> Acidaminococcus
<400> 13
taggtggcaa gcgttgtccg gaattattgg gcgtaaagag catgtaggcg ggcttttaag 60
tctgacgtga aaatgcgggg cttaaccccg tatggcgttg gatactggaa gtcttgagtg 120
caggagagga aaggggaatt cccagtgtag cggtgaaatg cgtagatatt gggaggaaca 180
ccagtggcga aggcgccttt ctggactgtg tctgacgctg agatgcgaaa gccagggtag 240
caaacgggat ta 252
<210> 14
<211> 253
<212> DNA
<213> mitochondria
<400> 14
tcctattcgc tccccatgct ttcgcacccc agcgtcggta gggacccaga gagctgcctt 60
cgcttttggc gttccttcgt agatctacgg atttcacccc tacacacgaa attccactct 120
cctctgtctc actcaagtga attggtttcg agagcattcc gcctgttttt ggcgactttc 180
actttcaacc cgattcaccg cctacgtccc ctttacgccc agtcattccg aagaacactt 240
gccccccccg tct 253
<210> 15
<211> 203
<212> DNA
<213> coprophilus
<400> 15
tcctgtttga tacccgcact ttcgagcctc aacgtcagtg gcggcttagc aggctgcctt 60
cgcaatcggg gttcttcgtg atatctaagc atttcaccgc tacaccacga attccgcctg 120
cctcaaccgt actcaaggtc tccagtttca actgcaattt taaggttgag ccccaaactt 180
tcacagctga cttaaaaacc cgt 203
<210> 16
<211> 254
<212> DNA
<213> Aeromonadaceae
<400> 16
tcctgtttgc tccccacgct ttcgcacctg agcgtcagtc tttgtccagg gggccgcctt 60
cgccaccggt attcctccag atctctacgc atttcaccgc tacacctgga attctacccc 120
cctctacaag actctagctg gacagtttta aatgcaattc ccaggttgag cccggggctt 180
tcacatctaa cttatccaac cgcctgcgtg cgctttacgc ccagtaattc cgattaacgc 240
ttgcacactc cgta 254
<210> 17
<211> 203
<212> DNA
<213> WAL_1855D
<400> 17
tcctgtttgc tccccacgct ttcgtgcctc agcgtcagtt caagtccaga aagtcgcctt 60
cgccaccggt attcctccta atatctacgc attccaccgc tacactagga attccacttt 120
cctctccttg actcaagcat atcagtttca gatgcaatct atgggttgag cccttagttt 180
tcacatctga cttaatttgc cgc 203
<210> 18
<211> 204
<212> DNA
<213> Staphylococcus
<400> 18
tacgtaggtg gcaagcgtta tccggaatta ttgggcgtaa agcgcgcgta ggcggttttt 60
taagtctgat gtgaaagccc acggctcaac cgtggagggt cattggaaac tggaaaactt 120
gagtgcagaa gaggaaagtg gaattccatg tgtagcggtg aaatgcgcag agatatggag 180
gaacaccagt ggcgaaggcg actc 204
<210> 19
<211> 203
<212> DNA
<213> Clostridium
<400> 19
tcctgtttgc tccccacgct ttcgagcctc agcgtcagtt acagtccaga gagtcgcctt 60
cgccactggt gttcttccta atctctacgc atttcaccgc tacactagga attccactct 120
cctctcctgc actctagata accagtttgg aatgcagcac ccaagttgag cccgggtatt 180
tcacatccca cttaatcatc cgc 203
<210> 20
<211> 254
<212> DNA
<213> ph2
<400> 20
tcctgtttgc tccccacgct ttcgtacctc agcgtcagtt gatatccaga cagtcgcctt 60
cgccaccggt attcctccta atctctacgc atttcaccgc tacactagga attccactgt 120
cccctctatc actcaaggtc accagtttct actgcttaca ggggttgagc ccctggcttt 180
cacaatagac ttaatgatcc gcctacgtac cctttacgcc caataattcc ggcccacgct 240
tgccccctac gtat 254
<210> 21
<211> 203
<212> DNA
<213> Micrococcus
<400> 21
tcctgttcgc tccccatgct ttcgcttctc agcgtcagtt acagcccaga gacctgcctt 60
cgccatcggt gttcctcctg atatctgcgc attccaccgc tacaccagga attccagtct 120
cccctaccgc actcaagccc gcccgtaccc ggcgcggatc caccgttaag cgatggactt 180
tcacaccgga cgcgacgaac cgc 203
<210> 22
<211> 204
<212> DNA
<213> moorei
<400> 22
tacgtaggtg gcgagcgtta tccggaatta ttgggcgtaa agggtgcgta ggcggcctgt 60
taagtaagtg gttaaattgt tgggctcaac ccaatccagc cacttaaact ggcaggctag 120
agtattggag aggcaagtgg aattccatgt gtagcggtgg aatgcgcaga tatcaggaag 180
aacaccggtg gcgaaggcgg gtct 204
<210> 23
<211> 203
<212> DNA
<213> pylori
<400> 23
tcctgtttgc tccccacgct ttcgcgcaat cagcgtcagt aatgttccag caggtcgcct 60
tcgcaatgag tattcctctt gatctctacg gattttaccc ctacaccaag aattccacct 120
acctctccca cactctagaa tagtagtttc aaatgcagtt ctatggttaa gccataggat 180
ttcacacctg actgactatc ccg 203
<210> 24
<211> 254
<212> DNA
<213> Helicobacteraceae
<400> 24
tacggagggt gcaagcgtta ctcggaatca ctgggcgtaa agagcacgta ggcggcctta 60
caagtcagat gtgaaatcta acggcttaac cgttaaactg catttgaaac tgtagggcta 120
gagtatggga gaggtaggtg gaattctcgg tgtaggggta aaatccgtag agatcgagag 180
gaatactcat tgcgaaggcg acatgctgga acattactga cgctctggtg cgaaagcgtg 240
gggagcaaac agga 254
<210> 25
<211> 204
<212> DNA
<213> Holdemania
<400> 25
tcctatttgc tccccacgct ttcgtgcatg agcgtcagtt acaggccagg caaccgcctt 60
cgccactggt gttcctccat atatctacgc attttaccgc tacacatgga attccattgc 120
cctctcctgt actctagtct gtcagtttct aaggctatat ggggttaagc cccacgcttt 180
caccttaaac ttaacaaacc gcct 204
<210> 26
<211> 203
<212> DNA
<213> Serratia
<400> 26
tcctgtttgc tccccacgct ttcgcacctg agcgtcagtc ttcgtccagg gggccgcctt 60
cgccaccggt attcctccag atctctacgc atttcaccgc tacacctgga attctacccc 120
cctctgacat actctagctt accagttcaa aacgcagttc ccaagttaag ctcggggatt 180
tcacatcttg cttaataaac cgt 203
<210> 27
<211> 202
<212> DNA
<213> SHA-98
<400> 27
tcctgttcgc tcccccagct ttcgcgcctc agcgtcagtt acagtccaga aagccgcctt 60
cgccaccggt attcctccca atatctacgc atttcaccgc tacactggga attctacttt 120
cctctcctgc actctagtcc gccagttttg aacgtcgccc cccagttgag ccggggtatt 180
tcacgtccaa cttaacgatc cg 202
<210> 28
<211> 201
<212> DNA
<213> Actinobacillus
<400> 28
tcctgtttgc tccccacgct ttcgcacatg agcgtcagta cattcccaag gggctgcctt 60
cgccttcggt attcctccac atctctacgc atttcaccgc tacacgtgga attctacccc 120
tccctaaagt actctagcga cccagtatga aatgcaattc ccaagttaag ctcagactta 180
tcaaaccgcc tgcgctcgct t 201
<210> 29
<211> 204
<212> DNA
<213> Azomonas
<400> 29
tacggagggt gcgagcgtta atcggaataa ctgggcgtaa agggcacgca ggcggtgact 60
taagtgaggt gtgaaagccc cgggcttaac ctgggaattg catccaaaac tactgagcta 120
gagtacggta gagggtggtg gaatttcctg tgtagcggtg aaatgcgtag atataggaag 180
gaacaccagt ggcgaaggcg acca 204
<210> 30
<211> 201
<212> DNA
<213> lautus
<400> 30
tcctgtttgc tccccacgct ttcgcgcctc agcgtcagtt acagcccaga gagtcgcctt 60
cgccactggt gttcctccac atatctacgc atttcaccgc tacacgtgga attccactct 120
cctcttctgc actcaagtcc cccagtttcc agtgcgaccc gaagttgagc ctcgggttta 180
aacaccagac ttaaagaacc g 201
<210> 31
<211> 202
<212> DNA
<213> torques
<400> 31
tatggtgcaa gcgttatccg gatttactgg gtgtaaaggg agcgtagacg gatgggcaag 60
tctgatgtga aaacccgggg ctcaaccccg ggactgcatt ggaaactgtt catctagagt 120
gctggagagg taagtggaat tcctagtgta gcggtgaaat tcgtagatat ttgtaggaat 180
gccgatgggg aagccagctt ac 202
<210> 32
<211> 201
<212> DNA
<213> Halomonadaceae
<400> 32
tcctgtttgc tacccacgct ttcgcacctc agtgtcagtg tcagtccaga aggccgcctt 60
cgccactggt attcctcccg atctctacgc atttcaccgc tacaccggga attctacctt 120
cctctcctgc actctagcca agcagttcca gatgccgttc ccaggttgag cccggggctt 180
tcacacctgg ctgacttagc c 201
<210> 33
<211> 200
<212> DNA
<213> Coprobacillus
<400> 33
taggtggcga gcgttatccg gaatcattgg gcgtaaagag ggagcaggcg gcaatagagg 60
tctgcggtga aagcctgaag ctaaacttca gtaagccgtg gaaaccaaat agctagagtg 120
cagtagagga tcgtggaatt ccatgtgtag cggtgaaatg cgtagatata tggaggaaca 180
ccagtggcga aggcgacgat 200

Claims (10)

1. a kind of microbial flora for oral precancerous lesion risk profile, including Healthy People enrichment flora and oral precancerous lesions Become patient and be enriched with flora, it is characterised in that:
Healthy People enrichment flora at least contains:
p__Firmicutes|c__Bacilli|o__Lactobacillales、f__Prevotellaceae|g__ Prevotella|s__nanceiensis、
f__Veillonellaceae|g__Megasphaera、c__TM7-3|o__CW040、
o__Clostridiales|f__Peptostreptococcaceae、o__Rickettsiales|f__ mitochondria、
f__Tissierellaceae|g__WAL_1855D、f__Staphylococcaceae|g__Staphylococcus、
f__Tissierellaceae|g__ph2、o__Campylobacterales|f__Helicobacteraceae、
g__Paenibacillus|s__lautus；
Oral precancerous lesion patient is enriched with flora and at least contains:
f__Micrococcaceae|g__Rothia|s__dentocariosa、f__Enterococcaceae|g__ Vagococcus、
f__Porphyromonadaceae|g__Paludibacter、
f__Flavobacteriaceae|g__Capnocytophaga|s__ochracea、
f__Prevotellaceae|g__Prevotella|s__stercorea、f__Halomonadaceae|g__ Halomonas、
c__Bacilli|o__Gemellales、f__Veillonellaceae|g__Acidaminococcus、
f__Bacteroidaceae|g__Bacteroides|s__coprophilus、o__Aeromonadales|f__ Aeromonadaceae、
f__Clostridiaceae|g__Clostridium、f__Micrococcaceae|g__Micrococcus、
f__Erysipelotrichaceae|g__Bulleidia|s__moorei、
f__Helicobacteraceae|g__Helicobacter|s__pylori、f__Erysipelotrichaceae|g__ Holdemania、
f__Enterobacteriaceae|g__Serratia、c__Clostridia|o__SHA-98、
f__Pasteurellaceae|g__Actinobacillus、f__Pseudomonadaceae|g__Azomonas、
g__Ruminococcus|s__torques、o__Oceanospirillales|f__Halomonadaceae、
f__Erysipelotrichaceae|g__Coprobacillus。
2. micropopulation according to claim 1, it is characterised in that: oral precancerous lesion risk is calculated by flora abundance It obtains, calculation formula is as follows:
In formula, IjFor risk index, AijIt is the relative abundance of strain i in sample j, selection where N is is rich in Healthy People flora Collecting the subset of flora, M is the subset with oral precancerous lesion relative enrichment flora, | N | with | M | it is the first subset and second respectively The number of strain described in subset.
3. a kind of kit for oral precancerous lesion risk profile, contains micropopulation described in quantitative claim 1 Reagent.
4. kit according to claim 3, it is characterised in that: oral precancerous lesion risk is calculated by flora abundance It arrives, calculation formula is as follows:
In formula, IjFor risk index, AijIt is the relative abundance of strain i in sample j, selection where N is is rich in Healthy People flora Collecting the subset of flora, M is the subset with oral precancerous lesion relative enrichment flora, | N | with | M | it is the first subset and second respectively The number of strain described in subset.
5. kit according to claim 4, it is characterised in that: | N |=11.
6. kit according to claim 4 or 5, it is characterised in that: | M |=22.
7. kit according to claim 4, it is characterised in that: IjCritical value are as follows: -1.834078~-2.133096.
8. kit according to claim 4, it is characterised in that: kit is PCR kit, the OTU sequence of amplification As shown in SEQ ID NO:1~SEQ ID NO:33.
9. the reagent of Quantitative microbial group's abundance exists preparing the application in oral precancerous lesion risk profile reagent, feature In: micropopulation is micropopulation described in claim 1.
10. application according to claim 9, it is characterised in that: the reagent of Quantitative microbial group's abundance is PCR reagent, The OTU sequence of amplification is as shown in SEQ ID NO:1~SEQ ID NO:33.
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