CN109182522B

CN109182522B - Microbiota for predicting oral cancer risk and application

Info

Publication number: CN109182522B
Application number: CN201811137118.3A
Authority: CN
Inventors: 宋卓; 卢秦; 孙海鹏
Original assignee: Genetalks Bio Tech Changsha Co ltd
Current assignee: Genetalks Bio Tech Changsha Co ltd
Priority date: 2018-09-28
Filing date: 2018-09-28
Publication date: 2022-06-28
Anticipated expiration: 2038-09-28
Also published as: CN109182522A

Abstract

The invention discloses a microbiota for predicting oral cancer risk and application, and the inventors identify a microbiota classification marker in 127 health controls and 125 oral cancer saliva sample sequencing data. Based on the microbiota of the present invention, oral cancer risk can be effectively predicted. The diagnostic reagent developed based on the microbial community can effectively predict the oral cancer risk, and has noninvasive detection process and high safety. Meanwhile, as the flora sample can be transported at normal temperature, the convenience of detection is greatly improved.

Description

Microbiota for predicting oral cancer risk and application

Technical Field

The invention relates to the use of markers and related agents for predicting the risk prediction of oral cancer.

Background

Oral cancer is one of the most prevalent cancers, more than eighty percent of which are oral squamous cell carcinomas, and the incidence of oral cancer is influenced by many factors, including smoking, drinking, viral infection and the like, and about 30 million people suffer from oral cancer every year. The onset of oral cancer is a progressive process, the early stage is often easily ignored by people, the optimal treatment time is delayed, and the current gold standard for oral cancer diagnosis is still through pathological diagnosis, so that the development of a new specific diagnosis molecular target is necessary.

Some studies on the relationship between oral flora and oral cancer have been made, such as:

xuyunlong, change of microorganisms in the region of Jinhua oral cancer [ J ] oral biomedicine 2016(2): 108-.

LeboLei, Lilongjiang, Zhoudong, et al oral microorganisms and oral cancer [ J ] oral medicine research, 2015,31(6): 558-. It is expressed as follows: the microbial community on the surface of the tumor tissue of the oral cancer has found that: the bacteria amount of veillonella, clostridium, porphyromonas, actinomyces, clostridium, haemophilus, enterobacter and streptococcus on the surface of the squamous cell carcinoma tissue is obviously increased; the microorganisms in the tumor tissue of the oral cancer have been found: the adjacent granule chain bacteria, the porphyromonas gingivalis, the Sphingomonas PC5.28, the light streptococcus and the oral streptococcus in the tumor tissues are relatively more; the microbial community in the saliva of tumor patients has found that: the predominant flora structure in saliva is different between oral cancer patients and non-tumor patients.

There is increasing evidence that oral cancer occurs in close relation to changes in oral microorganisms. In the early stage of oral cancer occurrence, namely in the stage of precancerous lesion, oral microorganisms have changed to some extent, so that the change of oral flora can be used as a new diagnostic molecular target of oral cancer or oral precancerous lesion.

Currently, genomics and proteomics are commonly used to develop biomarkers for oral cancer diagnosis. The invention patent 'a marker for detecting oral cancer and a kit thereof' (publication No. CN106636343A) discloses a marker for detecting oral cancer, which consists of four DNA replication initiation proteins, and the use of the marker as a detection target in the kit. The invention patent application of miR-518a-3p in oral cancer diagnosis (publication number CN107604066A) discloses application of a gene miR-518a-3p and a precursor thereof in preparation of an oral cancer diagnosis preparation. While the development of flora markers by means of microbiology is mostly applied to diseases such as colorectal cancer, ankylosing spondylitis and the like at present, most of researches on the aspects of microbial flora and oral cancer are on descriptive researches and mechanism conjectures. The prior art does not specify how to predict oral cancer risk by the profile of the microbial flora.

Disclosure of Invention

The present invention aims to overcome the deficiencies of the prior art and to provide a set of microbiota that can efficiently predict the risk of oral cancer.

It is another object of the present invention to provide use of an agent for quantifying abundance of microbiota in the preparation of a predicting agent for oral cancer risk.

The technical scheme adopted by the invention is as follows:

As a further improvement of the above microbiota, the oral cancer risk is calculated by the abundance of the microbiota, and the calculation formula is as follows:

in the formula I_jIs an index of oral health, A_ijIs the relative abundance of strain i in sample j, N is the site selectionEnriching a subset of the flora in healthy human flora; m is a subset of an enriched flora associated with oral cancer; and | N | and | M | are the number of the strains in the first subset and the second subset respectively.

A kit for oral cancer risk prediction comprising a reagent for quantifying the microbiota.

As a further improvement of the kit, the oral cancer risk is obtained by calculating the abundance of the flora, and the calculation formula is as follows:

in the formula I_jIs an index of oral health, A_ijIs the relative abundance of the strain i in the sample j, and N is the subset of the enriched flora in the healthy people flora; m is a subset of an enriched flora associated with oral cancer; and | N | and | M | are the number of the strains in the first subset and the second subset respectively.

As a further improvement of the above kit, | N | is 12.

As a further improvement of the above kit, | M | is 29.

As a further improvement of the above kit, I_jThe threshold value range of (2) is 2.396290-2.218559.

As a further improvement of the kit, the kit is a PCR kit, and the amplified OTU sequence is shown as SEQ ID NO: 1 to SEQ ID NO: shown at 41.

Use of a reagent for quantifying abundance of a microbiota in the preparation of a pre-test agent for oral cancer risk, the microbiota being as defined above.

As a further improvement of the application, the reagent is a PCR reagent, and the amplified OTU sequence is shown as SEQ ID NO: 1 to SEQ ID NO: shown at 41.

The invention has the beneficial effects that:

based on the microbiota of the present invention, oral cancer risk can be effectively predicted.

The diagnostic reagent developed based on the microbial community can effectively predict the oral cancer risk, and has noninvasive detection process and high safety. Meanwhile, as the flora sample can be transported at normal temperature, the convenience of detection is greatly improved.

Drawings

FIG. 1 is a graph of microbial distribution in a population;

FIG. 2 is a Receiver Operating Characteristic (ROC) curve of a health index;

FIG. 3 is a graph of health index distribution between oral cancer and healthy persons;

FIG. 4 is a ROC curve for a training set;

FIG. 5 is a ROC curve for the test set.

Detailed Description

Table 1 shows the enrichment of various species of microorganisms in the population:

TABLE 1 enrichment of different microorganisms in the human population

Table 2 shows the OTU sequence for each species of microorganism:

TABLE 2 OTU sequence cases for different microorganisms

Index of oral health

To exploit the potential of using microbiota for disease identification, the inventors developed a disease classification system based on defined genetic markers. In order to intuitively assess the risk of disease using these oral microbial gene markers, the inventors calculated an oral health index.

Index of oral health I_jThe calculation formula of (2) is as follows:

wherein A is_ijIs the relative abundance of species i in sample j, N is the subset of the population selected to be enriched in healthy human populations, and M is the subset of the population enriched for oral cancer. And | N | and | M | are the number of the strains in the first subset and the second subset respectively. Where | N | is 12 and | M | is 29.

The Receiver Operating Characteristic (ROC) curve analysis of the oral health index is shown in fig. 2. It can be seen that the classification is better when the threshold range is 2.396290-2.218559.

Table 3 is the oral health index for the specific sample.

TABLE 3 oral health index for specific samples

The distribution of health index between oral cancer population and healthy population is shown in fig. 3. As can be seen from the figure, the distribution of the health indexes in oral cancer people and healthy people is obviously different, and the oral cancer health index distinguishing method has good distinguishing capability.

To validate the potential of using the microbial flora for oral cancer classifiers, a disease classification system based on 41 genetic markers selected as the best gene set by the Lasso regression method was developed based on public database data.

And (3) verifying the effect of the classifier:

the data source is as follows: data were obtained by sequencing from 16S V3-V4 on saliva samples from the mouth of healthy persons and oral cancer patients collected in the early stage, and pre-processing of data by Usearch including removal of primers, removal of low quality reads, OTU clustering, etc., and OTU alignment using QIIME.

Analysis of factors affecting microbial distribution

The inventors used a permutation-multivariate analysis of variance (PERMANOVA) method to assess the effect of different characteristics on the oral flora, including age, gender, and disease state. The inventors performed analyses using the "Vegan" kit in R, and obtained a displacement p-value after 10000 displacements. The inventors also corrected the multiple tests using the "p.adjust" kit in R, using the FDR method to obtain p-values for each species. The results of the displacement multivariate analysis of variance are shown in table 4, and show that the effect of age and sex on oral flora is not significant, while the effect of disease state on oral flora is significant.

TABLE 4 Effect of different characteristics on the oral flora

	Degree of freedom	SumOfSqs	R2	F	Pr(>F)	p.adjust
							Disease(s)	1	0.529753	0.026058	6.713206	1.00E-04	0.0003
Sex	1	0.050275	0.002473	0.637101	0.813019	0.813019
							Age(s)	52	4.204167	0.206798	1.024551	0.357964	0.536946

The Lasso model (R glmnet 2.0-16) was trained using the abundance spectra of the training cohort, species markers were selected and tested in a test set, and prediction errors were calculated.

The Lasso model (R glmnet 2.0-16) is adopted for prediction, and the input is disease state and species abundance, and the disease state and the species abundance are divided into a training set and a testing set. The inventors constructed a classification using the glmnet package in the R software and predicted the test set using the prediction function, outputting a prediction result (probability of disease: default cutoff 0.5, greater than 0.5 then the subject is considered oral cancer). The training set and the test set are divided according to a 7:3 ratio, wherein 177 training sets and 75 test sets are used.

Following the Lasso regression calculations based on the microbiota of the present invention, ROC curves were plotted against the training set (177 cases) (fig. 4), with an area under the training set AUC curve of 0.971. The ROC curve for the test set (75 cases) is shown in fig. 5, and the test set has an area of 0.839 under the total AUC.

It can be known from the figure that the discrimination of the model built by using the variables selected by the Lasso regression is good no matter the model is a training set or a test set.

SEQUENCE LISTING

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<120> microbiota for predicting oral cancer risk and application thereof

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<210> 3

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tcccgttcgc tcccctggct ttcgcgcctc agcgtcagtt ttcgtccaga aagtcgcctt 60

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cctctccgat actctagatc agcagtttcc atcccatcac ggggttaagc cccgaacttt 180

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<210> 4

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tccggttcgc tccccacgct ttcgtgcctt agcgtcagaa atggcccagt aacctgccta 60

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cctctaccat tctcgagttt aacagtttga ataatagtct gtatggttga gccaccaggt 180

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gcttgcaaca tac 253

<210> 5

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<213> Granulicatella

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tacgtaggtg gcaagcgttg tccggattta ttgggcgtaa agcgagcgca ggcggttcct 60

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gagtgcagaa gaggagagtg gaattccatg tgtagcggtg aaatgcgtag atatatggag 180

gaacaccagt ggcgaaggcg aacc 204

<210> 6

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tcctgtttga tacccgcacc ttcgagcttc agcgtcagtt gtgctcccgt aagctgcctt 60

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cgttgtgcat actcaagtga accagttcgc gctgcaattc agacgttgag cgtctacatt 180

tcacaacacg cttaatccac gacctacgct ccctttaaac ccaataaatc cggataacgc 240

ctggaccttc cgta 254

<210> 7

<211> 254

<212> DNA

<213> endodontalis

<400> 7

tacggaggat acgagcgtta tccggaatta ttgggtttaa agggtgcgta ggtggcgtat 60

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gagaggagac gaggtatgcg gaatgtgtgg tgtagcggtg aaatgcatag atatcacaca 180

gaacgccgat tgcgaaggca gagtaccagg ctccgtctga cactgaagca cgaaaccgtg 240

gggatcaaaa cgga 254

<210> 8

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<400> 8

tacggagggt gcgagcgtta atcggaataa ctgggcgtaa agggcacgca ggcggctatt 60

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gagtacttta gggaggggta gaattccacg tgtagcggtg aaatgcgtag agatgtggag 180

gaataccgaa ggcgaaggca gacccttggg aatgtactga cgctcatgtg cgaaagcgtg 240

gggagcaaac agga 254

<210> 9

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<213> Campylobacter

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tacggagggt gcaagcgtta ctcggaatca ctgggcgtaa aggacgcgta ggcggattat 60

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gagtgaggga gaggcagatg gaattggtgg tgtaggggta aaatccgtag agatcaccag 180

gaatacccat tgcgaaggcg aactgctgga actcaactga cgctaatgcg tgaaagcgtg 240

gggagcaaac agga 254

<210> 10

<211> 254

<212> DNA

<213> noxia

<400> 10

tcccgttcgc tcccctggct ttcgagcctc agcgtcagtt gcagtccaga aagccgcctt 60

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cctctcctgt actcaagaca catagtttct ttcccatcac ggggttgagc cccgcacttt 180

taagaaagac ttacatgccc gcctgccctc cctttacgcc caataattcc ggccaacgct 240

cgccccatac gtat 254

<210> 11

<211> 254

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<400> 11

tacgtagggc gcgagcgttg tccggaatta ttgggcgtaa agagcttgta ggcggttggt 60

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gaacaccgat ggcgaaggca ggtcgcgggg ctgtaacgga cgctgagaag cgaaggcatg 240

gggggcgaaa agga 254

<210> 12

<211> 254

<212> DNA

<213> CW040

<400> 12

tccggttcgc tccccacgct ttcgtgcctc agtgtcagaa acagcccagt agcctgccta 60

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cctcttctgc tctcgagttc aacagttcga ataatagtct gaatggttga gccaccagat 180

ttcactattc gcttatcgaa caacctacgc aactctttac gcccagtcac tccggataac 240

gctcggatcc tacg 254

<210> 13

<211> 253

<212> DNA

<213> Dialister

<400> 13

tcctgtttgc tccccacgct ttcgcgcctc agcgtcagtt ttcgtccaga aagtcgcctt 60

cgccaccggt gttcttccta atctctacgc atttcaccgc tacactagga attccacttt 120

cctctccgat actccagctt cccagtttcc ttcccatcac ggggttaagc cccgcacttt 180

taagagggac ttgggaagcc gactgcgcgc gctttacgcc caataattcc ggacaaagct 240

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<210> 14

<211> 254

<212> DNA

<213> Vagococcus

<400> 14

tcctgtttgc tccccacgct ttcgcgcctc agtgtcagtt acaggccaaa aagccgcctt 60

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<210> 15

<211> 204

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<400> 15

tacgagggcc ccaagcgtta tccggaatta ctgggcgtaa agcgtctgta ggtggttttt 60

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agtgtgggag aggcaagcag aacggtatga gtaggggtgc aatccgttga tacataccag 180

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<211> 202

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tcctgtttgc tccccacgct ttcgcacatg agcgtcagta tcgacccaga aagctgcctt 60

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<210> 21

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<210> 22

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<210> 23

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<210> 24

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<210> 25

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<213> S24-7

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aactccgatt gcgaaggcag ccttccagtg catgactgac gctgaggcac gaaagcgtgg 240

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<210> 26

<211> 254

<212> DNA

<213> Sphaerochaeta

<400> 26

cacgtagggg gcgagcgttg ttcggaatca ttgggcgtaa agggtgcgca ggcggcttag 60

caagtctggt gtgaaatggt tgtgctcaac acaatcaggc gccggaaact gagaagctag 120

agtcaacgag gggatgccgg aattccaggt gtaggggtga aatctgtaga tatctggaag 180

aacaccaatg gcgaaggcag gcatctggcg atggactgac gctgaggcac gaaggtgcgg 240

ggagcaaaca ggtt 254

<210> 27

<211> 201

<212> DNA

<213> Rothia

<400> 27

gtgtgctctt ccgatctatc aggcagaagg actacccggg tatctaatcc tgttcgctcc 60

ccatgctttc gcttctcagc gtcagttaca gcccagagac ctgccttcgc catcggtgtt 120

cttcctgata tctgcgcatt ccaccgctac accaggagtt ccagtctccc ctactgcact 180

ctagtctgcc cgtacccact g 201

<210> 28

<211> 254

<212> DNA

<213> acnes

<400> 28

tacgtagggt gcgagcgttg tccggattta ttgggcgtaa agggctcgta ggtggttgat 60

cgcgtcggaa gtgtaatctt ggggcttaac cctgagcgtg ctttcgatac gggttgactt 120

gaggaaggta ggggagaatg gaattcctgg tggagcggtg gaatgcgcag atatcaggag 180

gaacaccagt ggcgaaggcg gttctctggg cctttcctga cgctgaggag cgaaagcgtg 240

gggagcgaac aggc 254

<210> 29

<211> 201

<212> DNA

<213> lividum

<400> 29

tccggttcgc tccccacgct ttcgtgcctt agcgtcagaa atggcccagt aacctgccta 60

cgccatcggt gttccttcta atatctacgg atttcaccgc tacacctgga attctacccc 120

cctctacaag actctagcct gccagtttcg aatgcagttc ccaggttgag cccggggatt 180

tcacatccga cttgacagac c 201

<210> 30

<211> 202

<212> DNA

<213> satelles

<400> 30

tcctgttcgc tccccatgct ttcgcttctc agcgtcagtt acagcccaga gacctgcctt 60

cgccatcggt gttcctcctg atatctgcgc attccaccgc tacaccagga attccgctta 120

cccctccgac actcgagctg aacagtttcc aatgcactac cggggttgag ccccgggctt 180

tcacatcaga cttgctctgc cg 202

<210> 31

<211> 203

<212> DNA

<213> Serratia

<400> 31

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> 32

<211> 254

<212> DNA

<213> Enhydrobacter

<400> 32

tacagagggt gcgagcgtta atcggaatta ctgggcgtaa agcgagcgta ggtggctaat 60

taagtcacat gtgaaatccc tgggcttaac ctaggaactg catgtgatac tggttagcta 120

gagtatgtga gaggggtgta gaattccagg tgtagcggtg aaatgcgtag agatctggag 180

gaataccgat ggcgaaggca gcaccctggc ataatactga cactgaggtt cgaaagcgtg 240

ggtagcaaac agga 254

<210> 33

<211> 204

<212> DNA

<213> Azomonas

<400> 33

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> 34

<211> 204

<212> DNA

<213> OPB56

<400> 34

tacgtaggat ccgagcgttg tccggaatta ctgggcgtaa agggtgcgta ggcggcaatg 60

tgcgtcagag gtgaaatcca cgggcttaac ttgtggggtg cctttgatac ggcatagctt 120

gagtacgaga gaggtcgatg gaattcctgg tgtagcagtg aaatgcgtag atatcaggag 180

gaacaccggt ggcgaaggcg gtcg 204

<210> 35

<211> 254

<212> DNA

<213> RFN20

<400> 35

tacgaagggt ccgagcgtta tccggaatta ttgggcgtaa agagtgagca ggcggccgtg 60

taagtccgag gtgaaagcgt ggggctcaac cccatacagc cccggaaact gcgcggctag 120

agtgcttgag aggtaaacgg aactccatgt gtagcggtga aatgcgtaga tatatggaag 180

aacaccggtg gcgaaggcgg tttaccagcg acgcactgac gctcagtcac gaaagcgtgg 240

ggagcaaata ggat 254

<210> 36

<211> 204

<212> DNA

<213> Cloacibacillus

<400> 36

tacgtagggg gcgagcgttg ttcggaatta ctgggcgtaa agcgcacgca ggcggaccag 60

taagtctgtc gtcaaaggcg gaggctcaac cttcgttcca cgatagatac tgcgggtcta 120

gagtatgtga gagggaagtg gaattcccgg tgtagcggtg aaatgcgtag agatgtggag 180

gaataccgaa ggcgaaggca gccc 204

<210> 37

<211> 254

<212> DNA

<213> metschnikovii

<400> 37

tacggagggt gcgagcgtta atcggaatta ctgggcgtaa agcgcatgca ggtggtttgt 60

taagtcagat gtgaaagccc ggggctcaac ctcggagttg catttgaaac tggcaggcta 120

gagtactgta gaggggggta gaatttcagg tgtagcggtg aaatgcgtag agatctgaag 180

gaataccggt ggcgaaggcg gccacctgga cagatactga cactcagatg ctaaagcgtg 240

gggagcaaac cgga 254

<210> 38

<211> 202

<212> DNA

<213> Succinivibrio

<400> 38

tacggagggt gcaagcgtta atcggaataa ctgggcgtaa agggcatgca ggcgggacgt 60

caagcagggt gtgaaatccc cgggctcaac ccgggaactg cactctgaac tgacgttctg 120

gagtatcgca gggggaggcg gaattccagg tgtagcggtg gaatgcgtag atatctggaa 180

gaacaccaaa ggcgaaggca gc 202

<210> 39

<211> 204

<212> DNA

<213> Sphingobium

<400> 39

gagggagcta gcgttgttcg gaattactgg gcgtaaagcg cacgtaggcg gcgatttaag 60

tcagaggtga aagcccgggg ctcaaccccg gaactgcctt tgagactgga ttgctagaat 120

cttggagagg cgagtggaat tccgagtgta gaggtgaaat tcgtagatat tcggaagaac 180

accagtggcg aaggcggctc gctg 204

<210> 40

<211> 201

<212> DNA

<213> Halomonadaceae

<400> 40

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> 41

<211> 201

<212> DNA

<213> Burkholderiaceae

<400> 41

tcctgtttgc tccccacgct ttcgtgcatg agcgtcagtg ttatcccagg aggctgcctt 60

cgccatcggt gttcctacgc atatcaacgc atttcactgc aacatgcgga attccacctc 120

cctctgacac actctagcct tgcagacacc aatgcagttc ccaggataag cccgcggatt 180

tcacatcggt attgcaaaac c 201

Claims

1. The application of the reagent for quantifying the abundance of the microbiota in preparing the oral cancer risk prediction reagent is characterized in that: the reagent for quantifying the abundance of the microbial population is a PCR reagent, and the amplified OTU sequence is shown as SEQ ID NO: 1 to SEQ ID NO: 41 is shown; the oral cancer risk is obtained by calculating the abundance of the flora, and the calculation formula is as follows:

wherein Ij is an oral health index, Aij is the relative abundance of the strain i in the sample j, N is a subset of the enriched flora selected in the healthy people, and M is a subset of the enriched flora associated with oral cancer; the | N | and the | M | are the number of the strains in the first subset and the second subset respectively; n | = 12; i M | = 29; the threshold range of Ij is: 2.396290-2.218559.