CN109371147A - A gut microbial gene marker for distinguishing liver cancer from non-hepatocellular carcinoma and its application - Google Patents
A gut microbial gene marker for distinguishing liver cancer from non-hepatocellular carcinoma and its application Download PDFInfo
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- CN109371147A CN109371147A CN201811297644.6A CN201811297644A CN109371147A CN 109371147 A CN109371147 A CN 109371147A CN 201811297644 A CN201811297644 A CN 201811297644A CN 109371147 A CN109371147 A CN 109371147A
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- 208000014018 liver neoplasm Diseases 0.000 title claims abstract description 150
- 201000007270 liver cancer Diseases 0.000 title claims abstract description 132
- 108700005443 Microbial Genes Proteins 0.000 title claims abstract description 44
- 239000003550 marker Substances 0.000 title claims abstract description 30
- 206010073071 hepatocellular carcinoma Diseases 0.000 title description 6
- 231100000844 hepatocellular carcinoma Toxicity 0.000 title description 3
- 244000005700 microbiome Species 0.000 claims abstract description 77
- 230000000968 intestinal effect Effects 0.000 claims abstract description 19
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 19
- 230000002550 fecal effect Effects 0.000 claims description 15
- 238000001514 detection method Methods 0.000 claims description 14
- 238000002790 cross-validation Methods 0.000 claims description 13
- 201000010099 disease Diseases 0.000 claims description 13
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 13
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- 108020004465 16S ribosomal RNA Proteins 0.000 claims description 7
- 238000007637 random forest analysis Methods 0.000 claims description 7
- 238000003066 decision tree Methods 0.000 claims description 6
- 230000036541 health Effects 0.000 claims description 6
- 210000004185 liver Anatomy 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 6
- 208000019425 cirrhosis of liver Diseases 0.000 claims description 5
- 206010028980 Neoplasm Diseases 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 201000011510 cancer Diseases 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 230000000813 microbial effect Effects 0.000 claims description 3
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- 239000003795 chemical substances by application Substances 0.000 claims 1
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- 108091008151 Ovarian Tumor Proteases Proteins 0.000 description 24
- 241000566145 Otus Species 0.000 description 22
- 102000038007 Ovarian Tumor Proteases Human genes 0.000 description 22
- 238000011160 research Methods 0.000 description 8
- 238000000034 method Methods 0.000 description 7
- 241000894006 Bacteria Species 0.000 description 6
- 241000095588 Ruminococcaceae Species 0.000 description 5
- 238000013459 approach Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 206010009944 Colon cancer Diseases 0.000 description 3
- 208000001333 Colorectal Neoplasms Diseases 0.000 description 3
- 206010016654 Fibrosis Diseases 0.000 description 3
- 230000007882 cirrhosis Effects 0.000 description 3
- 238000013399 early diagnosis Methods 0.000 description 3
- 230000002906 microbiologic effect Effects 0.000 description 3
- 208000024172 Cardiovascular disease Diseases 0.000 description 2
- 241000605861 Prevotella Species 0.000 description 2
- 206010052779 Transplant rejections Diseases 0.000 description 2
- 230000001154 acute effect Effects 0.000 description 2
- 230000003321 amplification Effects 0.000 description 2
- 238000007622 bioinformatic analysis Methods 0.000 description 2
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- 238000003745 diagnosis Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
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- 239000000463 material Substances 0.000 description 2
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- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 238000005086 pumping Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000002054 transplantation Methods 0.000 description 2
- 241000604451 Acidaminococcus Species 0.000 description 1
- 241001024600 Aggregatibacter Species 0.000 description 1
- 241000701474 Alistipes Species 0.000 description 1
- 241000606125 Bacteroides Species 0.000 description 1
- 241000186000 Bifidobacterium Species 0.000 description 1
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- 241001112695 Clostridiales Species 0.000 description 1
- 241000193403 Clostridium Species 0.000 description 1
- 241001464956 Collinsella Species 0.000 description 1
- 241001535083 Dialister Species 0.000 description 1
- 241000588921 Enterobacteriaceae Species 0.000 description 1
- 241001608234 Faecalibacterium Species 0.000 description 1
- 241000605909 Fusobacterium Species 0.000 description 1
- 241000711549 Hepacivirus C Species 0.000 description 1
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- 241000588748 Klebsiella Species 0.000 description 1
- 241000186660 Lactobacillus Species 0.000 description 1
- 241000043362 Megamonas Species 0.000 description 1
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- 241001267970 Paraprevotella Species 0.000 description 1
- 206010034647 Peristalsis visible Diseases 0.000 description 1
- 208000037581 Persistent Infection Diseases 0.000 description 1
- 241001453443 Rothia <bacteria> Species 0.000 description 1
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- 210000000936 intestine Anatomy 0.000 description 1
- 229940039696 lactobacillus Drugs 0.000 description 1
- 229940067606 lecithin Drugs 0.000 description 1
- 235000010445 lecithin Nutrition 0.000 description 1
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- -1 lecithin lipid Chemical class 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
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- 238000010837 poor prognosis Methods 0.000 description 1
- 210000000813 small intestine Anatomy 0.000 description 1
- 230000031068 symbiosis, encompassing mutualism through parasitism Effects 0.000 description 1
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Abstract
The invention belongs to biomedicine technical fields, and in particular to a kind of enteric microorganism gene marker and its application for distinguishing liver cancer and non-liver cancer.The present invention provides a kind of for distinguishing the enteric microorganism gene marker of liver cancer and non-liver cancer, and 30 kinds of microbial genes shown in SEQ ID NO:1-30 form, and the microbial gene is enriched in human body intestinal canal.Microbial gene difference model of the invention realizes good discrimination in liver cancer and non-liver cancer patient, also demonstrates feasibility, applicability and universality of the difference model in Chinese different geographical.
Description
Technical field
The invention belongs to biomedicine technical fields, and in particular to a kind of enteric microorganism base for distinguishing liver cancer and non-liver cancer
Because of marker and its application.
Background technique
Liver cancer (hepatocellular carcinoma, HCC) disease incidence in global malignant tumour occupies the 6th, death
Rate occupies second.China is liver cancer big country, and the highest country of global onset of liver cancer rate, and lung is only second in kinds of tumor
Cancer occupies second.Due to hepatitis B and hepatitis C virus persistent infection and its caused by cirrhosis prevalence, China's liver cancer
Incidence is serious.Onset of liver cancer number accounts for the 55% of the whole world at present, and death toll accounts for about whole world PLC mortality number
45%, it constitutes a serious threat to the health of our people.Since liver cancer onset is hidden, early diagnosis is difficult, and Most patients are true
Later period of hepatocarcinoma, poor prognosis have been reached when examining.It is even also easy to recur after operation excision, lack effective therapeutic agent and means.
Therefore, the diagnostic model for establishing difference liver cancer and non-liver cancer, realizes the early diagnosis of liver cancer, early detection, morning for liver cancer
Phase diagnosis and early treatment are of great significance.
Human body intestinal canal microecosystem and health and disease are closely related.Huge number (10 is colonized in human body intestinal canal14)、
Microbiologic population's (about 1.5 kilograms) that structure is complicated (more than 1000 kinds bacteriums).Its cell total amount is almost human body own cells
10 times of quantity, gene total amount are 150 times of mankind itself's gene.During intestinal flora and host's symbiosis and coevolution,
The digestion and absorption, immune response, metabolism etc. for adjusting host play a significant role.Intestinal microecology imbalance promotes chronic disease
Occurrence and development, including cirrhosis, hepatocellular carcinoma, cancer of pancreas and colorectal cancer etc..
Crux function bacterium can become the new biomarkers of human diseases in enteric microorganism.The feature of intestinal microecology
Or it is just more and more extensive as the differentiation tool of specified disease or tumour based on the difference model that enteric microorganism is established
Report and approval." visible peristalsis visible intestinal peristalsis (Enterotypes) " of intestinal flora can reflect that people to the neurological susceptibility of disease, prompts intestinal flora tool
There are potential early warning and diagnostic effect.Qin J etc. takes the lead in reporting intestinal microecology and diabetes B using metagenomics
Correlation, it is indicated that 23 strains are likely to become the enteric microorganism marker for distinguishing diabetes B.Qin N, Li A etc. (2014
Year Nature magazine) Chinese population liver cirrhosis patient micro-ecological in intestines parsed by macro genomic sequencing technique, micro-
On biological gene and functional level, 15 markers of cirrhosis specificity are identified, the liver for creating a high accuracy is hard
Change patient's discrimination index.Ren Z etc. points out that intestinal microecology reacts more sensitive, intestinal microecology to acute rejection of liver transplantation
Change Early acute rejection after can be used to predict liver transfer operation, improves the auxiliary that acute cellular rejection damages after liver transfer operation can also be become
Property target.Wang Z etc. (Nature, 2011) points out that the lecithin lipid metaboli dependent on intestinal microecology promotes cardiovascular disease
Progress, this is found to be the novel diagnostic method of cardiovascular disease and therapeutic strategy provides theoretical basis.Oh PL etc.
The variation of (AJT, 2012) discovery intestinal microecology is closely related with small intestine transplantation repulsion, can become a potential graft rejection
Diagnostic marker.Yu J etc. is disclosed in different nationalities patient and is demonstrated the microbial markers of colorectal cancer, it is indicated that micro-
A kind of early diagnosis marker of the biomarker as payable, noninvasive colorectal cancer.Thus, enteric microorganism may
It is the powerful of various disease diagnosis.However, for distinguishing liver cancer and the enteric microorganism model of non-liver cancer is not reported also
It crosses.
Summary of the invention
The present invention provides a kind of for distinguishing the enteric microorganism gene marker of liver cancer and non-liver cancer, by SEQ ID
30 kinds of microbial gene compositions, the microbial gene shown in NO:1-30 are enriched in human body intestinal canal.
In addition the present invention also provides one kind to be used for detection reagent, including for detecting 30 shown in SEQ ID NO:1-30
The primer of kind microbial gene, sequence are as follows:
>OTU_10
GTGAGGAATATTGGTCAATGGGCGAGAGCCTGAACCAGCCAAGTAGCGTGAAGGATGAAGGTCCTACGG
ATTGTAAACTTCTTTTATACGGGAATAAAGTTTCCTACGTGTAGGATTTTGTATGTACCGTATGAATAAGCATCGGC
TAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAG
ACGGGAGATTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGTTTCCTTGAGTGCAG
TTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACCCCGATTGCGAAGGCAGCTTG
CTAAACTGTAACTGACGTTCATGCTCGAAAGTGTGGGTATCAAACAGG
>OTU_12
GTGGGGAATATTGCACAATGGgggAAACCCTGATGCAGCAACGCCGCGTGAAGGATGACGGTTTTCGG
ATTGTAAACTTCTTTTCTTAGTGACGAAGACAGTGACGGTAGCTAAGGAATAAGCATCGGCTAACTACGTGCCAGCA
GCCGCGGTAATACGTAGGATGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTG
GATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAAT
TCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGAC
GCTGAGGCTCGAAAGTGTGGGGAGCAAACAGG
>OTU_28
GTGGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGACGGCCTTCGG
GTTGTAAAGCTCTGTGATCGGGGACGAATGGCTGGTATGCTAATACCATATCAGAGTGACGGTACCCGAATAGCAA
GCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAG
CgcgcgcAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGGGAGGCT
GGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAA
GGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG
>OTU_57
GTGGGGAATATTGCGCAATGGgggCAACCCTGACGCAGCAACGCCGCGTGCAGGAAGAAGGTCTTCGG
ATTGTAAACTGTTGTCGCAAGGGAAGAAGACAGTGACGGTACCTTGTGAGAAAGTCACGGCTAACTACGTGCCAGCA
GCCGCGGTAATACGTAGGTGACAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGCGTAGGCGGACTGTCAAGTCA
GTCGTGAAATACCGGGGCTTAACCCCGGGGCTGCGATTGAAACTGACAGCCTTGAGTATCGGAGAGGAAAGCGGAAT
TCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACGACAACTGAC
GCTGAGGCGCGAAAGTGTGGGGAGCAAACAGG
>OTU_58
GTGGGGAATATTGGGCAATGGGCGAAAGCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCCTTCGGG
TTGTAAACTTCTTTTAAGAGGGACGAAGAAGTGACGGTACCTCTTGAATAAGCCACGGCTAACTACGTGCCAGCAGC
CGCGGTAATACGTAGGTGGCGAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGTAGGCGGGAATGCAAGTCAGA
TGTGAAATCCAAGGGCTCAACCCTTGAACTGCATTTGAAACTGTATTTCTTGAGTGTCGGAGAGGTTGACGGAATTC
CTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGTCAACTGGACGATAACTGACGC
TGAGGCGCGAAAGCGTGGGGAGCAAACAGG
>OTU_63
GTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGG
GTTGTAAAGTACTTTCAGCGGGGAGGAAGGCGACAGGGTTAATAACCCTGTCGATTGACGTTACCCGCAGAAGAAG
CACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGC
GCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCT
AGAGTCTTGTAGAGGggggTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGA
AGGCGGCCcccTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGG
>OTU_78
GTGGGGAATTTTGGACAATGGgggCAACCCTGATCCAGCCATGCCGCGTGCAGGATGAAGGTCTTCGG
ATTGTAAACTGCTTTTGTCAGGGACGAAAAGGGATGCGATAACACCGCATTCCGCTGACGGTACCTGAAGAATAAG
CACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGC
GTGCGCAGGCGGTTCTGTAAGATAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATatatGACTGCAGGACT
TGAGTTTGTCAGAGGAGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGATATGTGGAAGAACACCGATGGCGAA
GGCAGCCCTCTGGGACATGACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACAGG
>OTU_86
GTGGGGAATATTGCACAATGGAGGAAACTCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGT
ATGTAAAGCTCTATCAGCAGGGAAGACAGTGACGGTACCTGACTAAGAAGCTCCGGCTAAATACGTGCCAGCAGCCG
CGGTAATACGTATGGAGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGTGTAGGTGGTATCACAAGTCAGAAG
TGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTTGAAACTGTGGAACTGGAGTGCAGGAGAGGTAAGTGGAATTCCT
AGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACTGTAACTGACACTG
AGGCTCGAAAGCGTGGGGAGCAAACAGG
>OTU_87
GTGGGGAATCTTGCGCAATGGggggAACCCTGACGCAGCGACGCCGCGTGCGGGACGGAGGCCTTCGG
GTCGTAAACCGCTTTCAGCAGGGAAGAGTCAAGACTGTACCTGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAG
CCGCGGTAATACGTAGGgggCGAGCGTTATCCGGATTCATTGGGCGTAAAGCgcgcgTAGGCGGCCCGGCAGGCCG
GgggTCGAAGCGGggggCTCAACCccccGAAGCCcccGGAACCTCCGCGGCTTGGGTCCGGTAGGGGAGGGTGGAA
CACCCGGTGTAGCGGTGGAATGCGCAGATATCGGGTGGAACACCGGTGGCGAAGGCGGCCCTCTGGGCCGAGACCG
ACGCTGAGGCGCGAAAGCTGGgggAGCGAACAGG
>OTU_96
GTGAGGAATATTGGTCAATGGACGAGAGTCTGAACCAGCCAAGTAGCGTGCAGGATGACGGCCCTATG
GGTTGTAAACTGCTTTTGCGCGGGGATAACACCCTCCACGTGCTGGAGGTCTGCAGGTACCGcgcgAATAAGGACC
GGCTAATTCCGTGCCAGCAGCCGCGGTAATACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGC
GTAGGCCGTGAGGTAAGCGTGTTGTGAAATGTAGGCGCCCAACGTCTGCACTGCAGCGCGAACTGCCCCACTTGAG
TGCgcgcAACGCCGGCGGAACTCGTCGTGTAGCGGTGAAATGCTTAGATATGACGAAGAACCCCGATTGCGAAGGC
AGCTGGCGGGAGCGTAACTGACGCTGAAGCTCGAAAGCGCGGGTATCGAACAGG
>OTU_97
GTGGGGAATATTGCACAATGGgggAAACCCTGATGCAGCGACGCCGCGTGAAGGAAGAAGTATCTCGG
TATGTAAACTTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGC
CGCGGTAATACGTAGGgggCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGATGGACAAGTCTG
ATGTGAAAGGCTGGGGCTCAACCCCGGGACTGCATTGGAAACTGCCCGTCTTGAGTGCCGGAGAGGTAAGCGGAATT
CCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACG
TTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
>OTU_128
GTGGGGAATATTGCGCAATGGgggAAACCCTGACGCAGCAACGCCGCGTGATTGAAGAAGGCCTTCGG
GTTGTAAAGATCTTTAATCAGGGACGAAACAAATGACGGTACCTGAAGAATAAGCTCCGGCTAACTACGTGCCAGC
AGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCgcgcAGGCGGGCCGGCAAGT
TGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGCAGGCGG
AATTCCGTgtgtAGCGGTGAAATGCGTAGATATACGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACATTAAC
TGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG
>OTU_136
GTAGGGAATCTTCCACAATGGACGCAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGTCTTCGG
ATCGTAAAACTCTGTTGTTAGAGAAGAACACGAGTGAGAGTAACTGTTCATTCGATGACGGTATCTAACCAGCAAG
TCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGG
GAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGGAGTAGTGCATTGGAAACTGGAAGACT
TGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATatatGGAAGAACACCAGTGGCGA
AAGCGGCTctctGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGTAGCAAACAGG
>OTU_209
GTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCGACGCCGCGTGAGGGATGGAGGCCTTCGGG
TTGTAAACCTCTTTTGTTTGGGAGCAAGCCTTCGGGTGAGTGTACCTTTCGAATAAGCGCCGGCTAACTACGTGCCA
GCAGCCGCGGTAATACGTAGGGCGCAAGCGTTATCCGGATTTATTGGGCGTAAAGGGCTCGTAGGCGGCTCGTCGCG
TCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCTGCGCCGGGTACGGGCGGGCTGGAGTGCGGTAGGGGAGACTGG
AATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCGATGGCGAAGGCAGGTCTCTGGGCCGTCACT
GACGCTGAGGAGCGAAAGCGTGGGGAGCGAACAGG
>OTU_285
GTGAGGAATCTTCCACAATGGGCGAAAGCCTGATGGAGCAACGCCGCGTGCAGGATGAAGGCCTTCGGG
TTGTAAACTGCTTTTATAAGTGAGGAATATGACGGTAGCTTATGAATAAGGATCGGCTAACTACGTGCCAGCAGCCG
CGGTCATACGTAGGATCCGAGCGTTATCCGGAGTGACTGGGCGTAAAGAGTTGCGTAGGCGGTTTGTTAAGTGAATA
GTGAAATCTGGTGGCTCAACCATACAGGCTATTATTCAAACTGGCAAACTCGAGAGTGGTAGAGGTCACTGGAATTT
CTTGTGTAGGAGTGAAATCCGTAGATATAAGAAGGAACACCGATGGCGTAGGCAGGTGACTGGACCATTTCTGACGC
TAAGGCACGAAAGCGTGGGGAGCGAACCGG
>OTU_291
GTGGGGAATATTGGGCAATGGgggAAACCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGTCTTCGG
ATCGTAAACTTCTATCCTCGGTGAAGAGGAGAAGACGGTAGCCGAGAAGGAAGCCCCGGCTAACTACGTGCCAGCA
GCCGCGGTAATACGTAGGgggCAAGCGTTGTCCGGAATGATTGGGCGTAAAGGGCGTGTAGGCGGCTAAGTAAGTC
TGGAGTGAAAGTCCTGCTTTTAAGGTGGGAATTGCTTTGGATACTGCATAGCTAGAGTGCAGGAGAGGTAAGTGGAA
TTCCCAGTGTAGCGGTGAAATGCGTAGAGATTGGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGTAACTGA
CGCTGAGGCGCGAAAGTGTGGGGAGCAAACAGG
>OTU_310
GTGGgggATATTGCACAATGGAGGAAACTCTGATGCAGCGACGCCGCGTGAGGGAAGACGGTCTTCGG
ATTGTAAACCTCTGTCTTTGGGGACGATAATGACGGTACCCAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCC
GCGGTAATACGTAGGGAGCGAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGTAGGCGGGGTCTCAAGTCGAAT
GTTAAATCTACCGGCTCAACTGGTAGCTGCGTTCGAAACTGGGGCTCTTGAGTGAAGTAGAGGCAGGCGGAATTCCT
AGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTTACTGACGCTG
AGGCTCGAAAGCGTGGGGAGCAAACAGG
>OTU_372
GTGGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGAAGGCCTTCGGG
TTGTAAAACTCTGTTGTCAGGGACGAACGTGCTGATTTACAATACACTTCAGCAGTGACGGTACCTGACGAGGAAGC
CACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGC
ATGTAGGCGGGCTTTTAAGTCCGACGTGAAAATGCGGGGCTTAACCCCGTATGGCGTTGGATACTGGAAGTCTTGAG
TGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCG
CCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGTAGCAAACGGG
>OTU_373
GTGGGGAATATTGGACAATGGACCAAAAGTCTGATCCAGCAATTCTGtgtgCACGATGACGTTtttCG
GAATGTAAAGTGCTTTCAGTTGGGAAGAAaaaaaTGACGGTACCAACAGAAGAAGTGACGGCTAAATACGTGCCAG
CAGCCGCGGTAATACGTATGTCACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGT
CTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAA
CTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGA
CGCTAAAGCGCGAAAGCGTGGGTAGCAAACAGG
>OTU_427
GTGGGGAATATTGCGCAATGGgggCAACCCTGACGCAGCCATGCCGCGTGAATGAAGAAGGCCTTCGG
GTTGTAAAGTTCTTTCGGTGACGAGGAAGGCGTGATGTTTAATAGGCATCACGATTGACGTTAATCACAGAAGAAGC
ACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCGAGCGTTAATCGGAATAACTGGGCGTAAAGGGC
ACGCAGGCGGCTATTTAAGTGAGGTGTGAAATCCCCGGGCTTAACCTGGGAATTGCATTTCAGACTGGGTAGCTAGA
GTACTTTAGGGAGGGGTAGAATTCCACGTGTAGCGGTGAAATGCGTAGAGATGTGGAGGAATACCGAAGGCGAAGGC
AGCCCCTTGGGAATGTACTGACGCTCATGTGCGAAAGCGTGGGGAGCAAACAGG
>OTU_451
GTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCGACGCCGCGTGAGGGATGACGGCCTTCGG
GTTGTAAACCTCTGTTAGCAGGGAAGAAGagagaTTGACGGTACCTGCAGAGAAAGCGCCGGCTAACTACGTGCCA
GCAGCCGCGGTAATACGTAGGGCGCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTTGTAGGCGGTTTGTCGCG
TCTGCTGTGAAAGGCCGGAGCTTAACTCCGTGTATTGCAGTGGGTACGGGCAGACTAGAGTGCAGTAGGGGAGACTG
GAATTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAGGAACACCGATGGCGAAGGCAGGTCTCTGGGCTGTAAC
TGACGCTGAGAAGCGAAAGCATGGGGAGCGAACAGG
>OTU_624
GTGAGGAATATTGGTCAATGGCCGCAAGTCTGAACCAGCCATGCCGCGTGCAGGATGACGGCTCTATG
AGTTGTAAACTGCTTTTGTACTAGGGTAAACTCACCTACGTGTAGGTGACTGAAAGTATAGTACGAATAAGGATCG
GCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATTCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCG
TAGGCGGTTTGATAAGTTAGAGGTGAAATGTTAGGGCTCAACCCTGAAACTGCCTCTAATACTGTTGGACTAGaga
gTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCA
GCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGGAGCAAACAGG
>OTU_664
GTGGGGAATCTTGCGCAATGGGCGAAAGCCTGACGCAGCCATGCCGCGTGAATGATGAAGGTCTTAGG
ATTGTAAAATTCTTTCACCGGGGACGATAATGACGGTACCCGGAGAAGAAGCCCCGGCTAACTTCGTGCCAGCAGC
CGCGGTAATACGAAGGgggCTAGCGTTGCTCGGAATTACTGGGCGTAAAGGGCGCGTAGGCGGATCGTTAAGTCAG
AGGTGAAATCCCAGGGCTCAACCCTGGAACTGCCTTTGATACTGGCGATCTTGAGTATGAgagagGTATGTGGAAC
TCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAGGCGACATACTGGCTCATTACTGAC
GCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG
>OTU_748
GTGGGGAATCTTCCGCAATGGGCGAAAGCCTGACGGAGCAACGCCGCGTGAACGATGAAGGTCTTAGGA
TCGTAAAGTTCTGTTGTTAGGGACGAAGGGCAAGGGTTATAATACAGCCTTTGTTTGACGGTACCTAACGAGGAAGC
CACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGCGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGA
GCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAG
TGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCG
GCTTTCTGGACTGTAACTGACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGG
>OTU_927
GTGGGGAATATTGGGCAATGGGCGAAAGCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCCTTCGG
GTTGTAAACTTCTTTTACCAGGGACGAAGGACGTGACGGTACCTGGAGAAaaaGCAACGGCTAACTACGTGCCAGC
AGCCGCGGTAATACGTAGGTTGCAAGCGTTGTCCGGATTTACTGAGTGTAAAGGGCGTGTAGGCGGAGATGCAAGTT
AGGAGTGAAATCTGTGGGCTCAACCCATAAACTGCTTCTAAAACTGTATCCCTTGAGTATCGGAGAGGCAAGCGGAA
TTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACGACAACTGA
CGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG
>OTU_968
GTGAGGAATATTGGTCAATGGGCGAGAGTCTGAACCAGCCAAGTAGCGTGCAGGATGACGGCCCTATG
GGTTGTAAACTGCTTTTATAAGGGAATAAAGTGAGCTACGTGTAGCTTtttGCATGTACCTTATGAATAAGGACCG
GCTAATTCCGTGCCAGCAGCCGCGGTAATACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGT
AGGCCGTCTTATAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTAC
GCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCT
CACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGG
>OTU_976
GTGAGGAATATTGGTCAATGGACGAGAGTCTGAACCAGCCAAGTAGCGTGCAGGATGACGGCCCTATG
GGTTGTAAACTGCTTTTATAAGGGAATAAAGTGAGTCTCGTGAGACTTtttGCATGTACCTTATGAATAAGGACCG
GCTAATTCCGTGCCAGCAGCCGCGGTAATACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGT
AGGCGGGCTTTTAAGTCAGCGGTCAAATGTCGTGGCTCAACCATGTCAAGCCGTTGAAACTGTAAGCCTTGAGTCTG
CACAGGGCACATGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTC
ACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGG
>OTU_1032
GTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTgtgtgAAGAAGGCCTTCGG
GTTGTAAAGCACTTTCAGCGGGGAGGAAGGCGGTGAGGTTAATAACCTCACCGATTGACGTTACCCGCAGAAGAAG
CACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGC
GCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCT
AGAGTCTTGTAGAGGggggTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGA
AGGCGGCCcccTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGG
>OTU_1091
GTGGgggATATTGGTCAATGGgggAAACCCTGAACCAGCAATGCCGCGTGAGGGAAGAAGGTCTTCGG
ATTGTAAACCTAAGTAGTCAGGGACGAAGAAAGTGACGGTACCTGAAGAGTAAGCTCCGGCTAACTACGTGCCAGCA
GCCGCGGTAATACGTAGGGAGCGAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGGTCGGCAAGTCA
GATGTGAAATACCGGGGCTTAACTCCGGGGCTGCATTTGAAACTGTTGATCTTGAGTGAAGTAGAGGCAGGCGGAAT
TCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGAC
GCTGAGGCACCAAAGCATGGGGAGCAAACAGG
>OTU_1294
GTGGGGAATATTGCACAATGGgggAAACCCTGATGCAGCGACGCCGCGTGGAGGAAGAAGGTCTTCGG
ATTGTAAACTCCTGTTGTTGGGGAAGATAATGACGGTACCCAACAAGGAAGTGACGGCTAACTACGTGCCAGCAGC
CGCGGTAAAACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGG
AAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTtttCTTGAGTAGTGCAGAGGTAGGCGGAAT
TCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGTAGCGCAACTGAC
GCTGAAGCTCGAAAGCGTGGGTATCGAACAGG
The primer sequence is SEQ ID NO:31-32:
Primer Primers
Region V3+V4:338F-806R is sequenced
Upstream primer: 338F ACTCCTACGGGAGGCAGCA
Downstream primer: 806R GGACTACHVGGGTWTCTAAT
It is described the present invention also provides application of the detection reagent in preparation liver cancer and the differentiation detection kit of non-liver cancer
Detection reagent is suitable for 30 kinds of microbial genes shown in detection SEQ ID NO:1-30.
Detection reagent of the present invention is used to establish a kind of enteric microorganism model for distinguishing liver cancer and non-liver cancer.
The microbial gene model is suitable for difference liver cancer patient and non-liver cancer patient, and wherein non-liver cancer patient includes liver
Sclerosis patients and normal healthy controls crowd.
Detection reagent detects the excrement of the object, to determine whether the sample includes SEQ ID NO:1-30
Shown in 30 kinds of microbial genes, if can establish difference liver cancer and non-liver cancer enteric microorganism genetic model.
By collecting the fecal sample into group objects, microorganism total DNA is extracted, the 16S rDNA of microbial DNA is completed
Miseq sequencing, detects whether that there are 30 kinds of microbial genes shown in SEQ ID NO:1-30.
Further, by collecting the fecal sample into group objects, microorganism total DNA is extracted, the 16S of intestinal flora is carried out
RDNA Miseq sequencing.Based on high-flux sequence data, liver cancer and the microorganism difference of non-liver cancer patient are established in training set
Model establishes liver cancer illness rate (probability of disease, POD) index；POD index is in verifying its area of centralized calculation
Other ability, is verified；It is further concentrated in the independent diagnostics from different geographical and carries out individual authentication, realize microbial gene
Distinguish universality of the model in Chinese liver cancer crowd.
It specifically includes:
(1) fecal sample into group objects (liver cancer patient and non-liver cancer patient) is collected, according to the standard method for extracting of DNA
The extracting for completing microorganism total DNA in fecal sample completes the 16S rDNA high of intestinal flora in Illumina MiSeq platform
Flux examining order；
(2) high-flux sequence data are based on, it is non-in 75 liver cancer and 105 in the training set of microorganism difference model
Between liver cancer patient, identified by the algorithm of five times of cross validation for the model based on a Random Forest model
Optimal 30 microbial gene markers.
(3) 30 microbial gene markers are based on, liver cancer is calculated by using the ratio of the decision tree generated at random
Illness rate (Probability of disease, POD) index.
(4) discrimination of the microorganism difference model between 75 liver cancer and 105 non-liver cancers reaches 80.64%,
POD index is significantly raised in liver cancer patient, has significant difference (p=1.5x10 between two groups-14)。
(5) it is concentrated in verifying, which distinguishes difference of the model between 30 early liver cancers and 56 normal healthy controls
Ability reaches 76.80%；POD index is significantly raised in early liver cancer patient, has significant difference (p=between two groups
2.2x10-7)。
(6) it is concentrated in verifying, which distinguishes model in the area between 45 advanced liver cancers and 56 normal healthy controls
Other ability reaches 80.40%；POD index is late significantly raised in liver cancer patient, has significant difference (p=between two groups
2.3x10-6)。
(7) it is concentrated in independent diagnostics, which distinguishes model strong in 18 liver cancer patients from Xinjiang and 56
Discrimination between health control reaches 79.20%；POD index is significantly raised in the liver cancer patient of Xinjiang region, between two groups
There is significant difference (p=0.00021).
(8) it is concentrated in independent diagnostics, which distinguishes model strong in 80 liver cancer patients from Zhengzhou and 56
Discrimination between health control is up to 81.70%；POD index is significantly raised in the liver cancer patient of Zhengzhou area, between two groups
There is significant difference (p=1.6x10-11)。
Therefore, microbial gene of the invention difference model realizes good difference energy in liver cancer and non-liver cancer patient
Power also demonstrates feasibility, applicability and universality of the difference model in Chinese different geographical.
In addition, additionally provide it is a kind of for distinguishing the kit of the enteric microorganism model of liver cancer and non-liver cancer, including with
In the primer for detecting 30 kinds of microbial genes shown in SEQ ID NO:1-30 described in claim 1.
It is of the invention specific steps are as follows:
(1) according to the design principle of prospective clinical trial, researching and designing of the invention is as shown in Figure 1.The research approach
The approval of the first affiliated hospital of Zhengzhou University and Ethics Committee of the first affiliated hospital of Medical College of Zhejiang Univ. is obtained.It is all enter
The patient of group signs research approach informed consent form and clinical sample collects informed consent form.
(2) each enrolled liver cancer patient and non-liver cancer patient provide a fresh fecal sample, research experiment people
Sample is distributed into 10 parts according to the weight of every part of 200mg by member, and is frozen immediately in -80 DEG C of refrigerators.The pumping of fecal bacteria total DNA
Method is proposed to carry out according to the specification of kit.
(3) amplification and DNA library building of fecal bacteria total DNA sample are completed, it is complete in IlluminaMiseq microarray dataset
It is sequenced at 16S rDNA.All output sequences complete basic pretreatment and basic bioinformatic analysis.
(4) from all samples random selection equivalent sequence number, be spliced into corresponding 16S according to UPARSE pipeline
RDNA gene order taxon (Operational Taxonomy Units, OTUs).It is instructed according to microbial gene marker
Practice collection, microbial gene marker verifying collection and microbial gene marker independent diagnostics collection, by all samples of generation
OTUs gene order is compiled.Based on microbial gene sequences, 2.6 release note of RDP classifier is used.
(5) representative series generated based on high-flux sequence data calculate microbial gene marker discovery collection
The OTUs frequency file of OTUs frequency file, the OTUs frequency file of verifying collection and independent diagnostics collection.These OTUs are used for one
Correlation research identifies the apparent OTUs abundance of difference between liver cancer patient and non-liver cancer patient.It is examined using Wilcoxon
Method statistic analyzes the microbial gene marker of difference between liver cancer patient and non-liver cancer patient.57 OTUs selected are micro-
Biological gene marker is further analyzed.
(6) in the training set of microorganism difference model, including 75 liver cancer patients and 105 non-liver cancer patients, it uses
The 57 OTUs abundance files filtered out, at a Random Forest model (R software 3.4.1 and random forest software package 4.6-12)
The middle algorithm (other than setting " importance=TRUE ", software parameter default) using five times of cross validations carries out micro- life
The screening of object gene marker.Using 5 tests of five times of cross validations, cross validation error curve is obtained, wherein minimum
Cross validation erroneous point used as cut-off value.The smallest cross validation error value is plus the standard deviation of respective value
Cut-off value.The set of 30 OTUs markers below of the error rate less than cut-off value is filtered out, minimum number is selected
Set of the set of mesh OTUs as optimal microbial gene marker, finally identifies optimal 30 for the model
Microbial gene marker (Fig. 2).The gene order for the 30 microorganism OTUs markers selected is shown in SEQ ID NO:1-30.
(7) calculated by using the ratio of the decision tree generated at random illness rate (Probability of disease,
POD) index.Decision tree forecast sample is " HCC ", the parameter prediction of setting are as follows: proximity=T, norm.votes=T,
Predict.all=TRUE.150 Random Forest models constructed in LOO mode are for predicting that each sample is concentrated in verifying
This POD index, finally calculates the POD index of the consensus forecast of each sample.
(8) receiver operating curves (ROC) is calculated using the pROC tool in R 3.3.0 program bag, for assessing micro- life
Object distinguishes model, and area under the curve (AUC) is used to specify the effect value of ROC.
(9) discrimination of the microorganism difference model between 75 liver cancer and 105 non-liver cancers reaches 80.64%
(Fig. 3), POD index is significantly raised in liver cancer patient, has significant difference (p=1.5x10 between two groups-14) (Fig. 4).
(10) it is concentrated in verifying, POD index is significantly raised in 30 early liver cancer patients, has conspicuousness poor between two groups
Different (p=2.2x10-7) (Fig. 5), difference energy of the microorganism difference model between 30 early liver cancers and 56 normal healthy controls
Power reaches 76.80% (Fig. 6).
(11) it is concentrated in verifying, POD index is significantly raised in 45 mid and late liver cancer patients, has conspicuousness between two groups
Difference (p=2.3x10-6) (Fig. 5), microorganism difference model is between 45 mid and late liver cancers and 56 normal healthy controls
Discrimination reaches 80.40% (Fig. 7).
(12) it is concentrated in independent diagnostics, POD index is significantly raised in 18 liver cancer patients from Xinjiang region, and two groups
Between have significant difference (p=0.00021) (Fig. 5), the microorganism distinguish model in 18 liver cancer patients from Xinjiang
Discrimination between 56 normal healthy controls reaches 79.20% (Fig. 8).
(13) it is concentrated in independent diagnostics, POD index is significantly raised in 80 liver cancer patients of Zhengzhou area, between two groups
There is significant difference (p=1.6x10-11) (Fig. 5), microorganism difference model is in 80 liver cancer patients and 56 from Zhengzhou
Discrimination between example normal healthy controls is up to 81.70% (Fig. 9).
Therefore, microbial gene of the invention difference model realizes good difference energy in liver cancer and non-liver cancer patient
Power also demonstrates feasibility, applicability and universality of the difference model in Chinese different geographical.
Detailed description of the invention
Fig. 1 is a kind of for distinguishing the researching and designing and clinical application of the enteric microorganism model of liver cancer and non-liver cancer.
Fig. 2 uses the optimal enteric microorganism genetic marker of five times of cross-validation methods identification based on Random Forest model
Object.
For Fig. 3 in the training set of 75 liver cancer and 105 non-liver cancers, microbial gene distinguishes the difference effect of model realization
Energy；
Fig. 4 is in the training set of 75 liver cancer and 105 non-liver cancers, the expression of illness rate (POD) index between the two groups
Difference；
Fig. 5 is concentrated in verifying collection and independent diagnostics, and compared with 56 normal healthy controls crowds, illness rate (POD) index is 30
Example early liver cancer, 45 mid and late liver cancers, 18 Xinjiang liver cancer samples and 80 Zhengzhou liver cancer samples differential expression；
Fig. 6 is concentrated in verifying, difference of illness rate (POD) index between 56 normal healthy controls and 30 early liver cancers
Ability；
Fig. 7 is concentrated in verifying, area of illness rate (POD) index between 56 normal healthy controls and 45 mid and late liver cancers
Other ability；
Fig. 8 is concentrated in independent diagnostics, and illness rate (POD) index is in 56 normal healthy controls and 18 liver cancer from Xinjiang
Between discrimination；
Fig. 9 is concentrated in independent diagnostics, and illness rate (POD) index is in 56 normal healthy controls and 80 liver cancer from Zhengzhou
Between discrimination；
Specific embodiment
Below with reference to embodiment, the invention will be further elaborated, but protection content of the invention is not limited only to these realities
Apply example.
Method therefor is conventional method unless otherwise instructed in the following example.Required material in the following example
Material or reagent are that public commercial source obtains unless otherwise specified.
The present invention passes through the fecal sample collected into group objects, extracts microorganism total DNA, carries out the 16S of intestinal flora
RDNA Miseq sequencing.Based on high-flux sequence data, liver cancer and the microorganism difference of non-liver cancer patient are established in training set
Model establishes liver cancer illness rate (probability of disease, POD) index；POD index is in verifying its area of centralized calculation
Other ability, is verified；It is further concentrated in the independent diagnostics from different geographical and carries out individual authentication, realize microbial gene
Distinguish universality of the model in Chinese liver cancer crowd.
Its operating procedure is as follows:
(1) according to the design principle of prospective clinical trial, researching and designing of the invention is as shown in Figure 1.The research approach
The approval of the first affiliated hospital of Zhengzhou University and Ethics Committee of the first affiliated hospital of Medical College of Zhejiang Univ. is obtained.It is all enter
The patient of group signs research approach informed consent form and clinical sample collects informed consent form.
(2) each enrolled liver cancer patient and non-liver cancer patient provide a fresh fecal sample, research experiment people
Sample is distributed into 10 parts according to the weight of every part of 200mg by member, and is frozen immediately in -80 DEG C of refrigerators.The pumping of fecal bacteria total DNA
Method is proposed to carry out according to the specification of kit.
(3) amplification and DNA library building of fecal bacteria total DNA sample are completed, it is complete in IlluminaMiseq microarray dataset
It is sequenced at 16S rDNA.All output sequences complete basic pretreatment and basic bioinformatic analysis.
(4) from all samples random selection equivalent sequence number, be spliced into corresponding 16S according to UPARSE pipeline
RDNA gene order taxon (Operational Taxonomy Units, OTUs).It is instructed according to microbial gene marker
Practice collection, microbial gene marker verifying collection and microbial gene marker independent diagnostics collection, by all samples of generation
OTUs gene order is compiled.Based on microbial gene sequences, 2.6 release note of RDP classifier is used.
(5) representative series generated based on high-flux sequence data calculate microbial gene marker discovery collection
The OTUs frequency file of OTUs frequency file, the OTUs frequency file of verifying collection and independent diagnostics collection.These OTUs are used for one
Correlation research identifies the apparent OTUs abundance of difference between liver cancer patient and non-liver cancer patient.It is examined using Wilcoxon
Method statistic analyzes the microbial gene marker of difference between liver cancer patient and non-liver cancer patient.57 OTUs selected are micro-
Biological gene marker is further analyzed.
(6) in the training set of microorganism difference model, including 75 liver cancer patients and 105 non-liver cancer patients, it uses
The 57 OTUs abundance files filtered out, at a Random Forest model (R software 3.4.1 and random forest software package 4.6-12)
The middle algorithm (other than setting " importance=TRUE ", software parameter default) using five times of cross validations carries out micro- life
The screening of object gene marker.Using 5 tests of five times of cross validations, cross validation error curve is obtained, wherein minimum
Cross validation erroneous point used as cut-off value.The smallest cross validation error value is plus the standard deviation of respective value
Cut-off value.The set of 30 OTUs markers below of the error rate less than cut-off value is filtered out, minimum number is selected
Set of the set of mesh OTUs as optimal microbial gene marker, finally identifies optimal 30 for the model
Microbial gene marker (Fig. 2).The gene order for the 30 microorganism OTUs markers selected is shown in SEQ ID NO:1-30.
(7) calculated by using the ratio of the decision tree generated at random illness rate (Probability of disease,
POD) index.Decision tree forecast sample is " HCC ", the parameter prediction of setting are as follows: proximity=T, norm.votes=T,
Predict.all=TRUE.150 Random Forest models constructed in LOO mode are for predicting that each sample is concentrated in verifying
This POD index, finally calculates the POD index of the consensus forecast of each sample.
(8) receiver operating curves (ROC) is calculated using the pROC tool in R 3.3.0 program bag, for assessing micro- life
Object distinguishes model, and area under the curve (AUC) is used to specify the effect value of ROC.
(9) discrimination of the microorganism difference model between 75 liver cancer and 105 non-liver cancers reaches 80.64%
(Fig. 3), POD index is significantly raised in liver cancer patient, has significant difference (p=1.5x10 between two groups-14) (Fig. 4).
(10) it is concentrated in verifying, POD index is significantly raised in 30 early liver cancer patients, has conspicuousness poor between two groups
Different (p=2.2x10-7) (Fig. 5), difference energy of the microorganism difference model between 30 early liver cancers and 56 normal healthy controls
Power reaches 76.80% (Fig. 6).
(11) it is concentrated in verifying, POD index is significantly raised in 45 mid and late liver cancer patients, has conspicuousness between two groups
Difference (p=2.3x10-6) (Fig. 5), microorganism difference model is between 45 mid and late liver cancers and 56 normal healthy controls
Discrimination reaches 80.40% (Fig. 7).
(12) it is concentrated in independent diagnostics, POD index is significantly raised in 18 liver cancer patients from Xinjiang region, and two groups
Between have significant difference (p=0.00021) (Fig. 5), the microorganism distinguish model in 18 liver cancer patients from Xinjiang
Discrimination between 56 normal healthy controls reaches 79.20% (Fig. 8).
(13) it is concentrated in independent diagnostics, POD index is significantly raised in 80 liver cancer patients of Zhengzhou area, between two groups
There is significant difference (p=1.6x10-11) (Fig. 5), microorganism difference model is in 80 liver cancer patients and 56 from Zhengzhou
Discrimination between example normal healthy controls is up to 81.70% (Fig. 9).
Therefore, microbial gene of the invention difference model realizes good difference energy in liver cancer and non-liver cancer patient
Power also demonstrates feasibility, applicability and universality of the difference model in Chinese different geographical crowd.
Sequence table
<110>Joseph Yam
Li Ang
<120>a kind of enteric microorganism model and its clinical application for distinguishing liver cancer and non-liver cancer
<130> 21-2018-2901
<160> 32
<170> SIPOSequenceListing 1.0
<210> 1
<211> 425
<212> DNA
<213>enteric microorganism (Bacteroides)
<400> 1
gtgaggaata ttggtcaatg ggcgagagcc tgaaccagcc aagtagcgtg aaggatgaag 60
gtcctacgga ttgtaaactt cttttatacg ggaataaagt ttcctacgtg taggattttg 120
tatgtaccgt atgaataagc atcggctaac tccgtgccag cagccgcggt aatacggagg 180
atgcgagcgt tatccggatt tattgggttt aaagggagcg cagacgggag attaagtcag 240
ttgtgaaagt ttgcggctca accgtaaaat tgcagttgat actggtttcc ttgagtgcag 300
ttgaggcagg cggaattcgt ggtgtagcgg tgaaatgctt agatatcacg aagaaccccg 360
attgcgaagg cagcttgcta aactgtaact gacgttcatg ctcgaaagtg tgggtatcaa 420
acagg 425
<210> 2
<211> 408
<212> DNA
<213>enteric microorganism (Clostridium IV)
<400> 2
gtggggaata ttgcacaatg ggggaaaccc tgatgcagca acgccgcgtg aaggatgacg 60
gttttcggat tgtaaacttc ttttcttagt gacgaagaca gtgacggtag ctaaggaata 120
agcatcggct aactacgtgc cagcagccgc ggtaatacgt aggatgcaag cgttatccgg 180
atttactggg tgtaaaggga gcgcaggcgg gactgcaagt tggatgtgaa ataccgtggc 240
ttaaccacgg aactgcatcc aaaactgtag ttcttgagtg aagtagaggc aagcggaatt 300
ccgagtgtag cggtgaaatg cgtagatatt cggaggaaca ccagtggcga aggcggcttg 360
ctgggcttta actgacgctg aggctcgaaa gtgtggggag caaacagg 408
<210> 3
<211> 430
<212> DNA
<213>enteric microorganism (Dialister)
<400> 3
gtggggaatc ttccgcaatg gacgaaagtc tgacggagca acgccgcgtg agtgatgacg 60
gccttcgggt tgtaaagctc tgtgatcggg gacgaatggc tggtatgcta ataccatatc 120
agagtgacgg tacccgaata gcaagccacg gctaactacg tgccagcagc cgcggtaata 180
cgtaggtggc aagcgttgtc cggaattatt gggcgtaaag cgcgcgcagg cggcttctta 240
agtccatctt aaaagtgcgg ggcttaaccc cgtgatggga tggaaactgg gaggctggag 300
tatcggagag gaaagtggaa ttcctagtgt agcggtgaaa tgcgtagaga ttaggaagaa 360
caccggtggc gaaggcgact ttctggacga caactgacgc tgaggcgcga aagcgtgggg 420
agcaaacagg 430
<210> 4
<211> 408
<212> DNA
<213>enteric microorganism (Ruminococcaceae)
<400> 4
gtggggaata ttgcgcaatg ggggcaaccc tgacgcagca acgccgcgtg caggaagaag 60
gtcttcggat tgtaaactgt tgtcgcaagg gaagaagaca gtgacggtac cttgtgagaa 120
agtcacggct aactacgtgc cagcagccgc ggtaatacgt aggtgacaag cgttgtccgg 180
atttactggg tgtaaagggc gcgtaggcgg actgtcaagt cagtcgtgaa ataccggggc 240
ttaaccccgg ggctgcgatt gaaactgaca gccttgagta tcggagagga aagcggaatt 300
cctagtgtag cggtgaaatg cgtagatatt aggaggaaca ccagtggcga aggcggcttt 360
ctggacgaca actgacgctg aggcgcgaaa gtgtggggag caaacagg 408
<210> 5
<211> 407
<212> DNA
<213>enteric microorganism (Ruminococcaceae)
<400> 5
gtggggaata ttgggcaatg ggcgaaagcc tgacccagca acgccgcgtg aaggaagaag 60
gccttcgggt tgtaaacttc ttttaagagg gacgaagaag tgacggtacc tcttgaataa 120
gccacggcta actacgtgcc agcagccgcg gtaatacgta ggtggcgagc gttatccgga 180
tttactgggt gtaaagggcg cgtaggcggg aatgcaagtc agatgtgaaa tccaagggct 240
caacccttga actgcatttg aaactgtatt tcttgagtgt cggagaggtt gacggaattc 300
ctagtgtagc ggtgaaatgc gtagatatta ggaggaacac cagtggcgaa ggcggtcaac 360
tggacgataa ctgacgctga ggcgcgaaag cgtggggagc aaacagg 407
<210> 6
<211> 430
<212> DNA
<213>enteric microorganism (Enterobacteriaceae)
<400> 6
gtggggaata ttgcacaatg ggcgcaagcc tgatgcagcc atgccgcgtg tatgaagaag 60
gccttcgggt tgtaaagtac tttcagcggg gaggaaggcg acagggttaa taaccctgtc 120
gattgacgtt acccgcagaa gaagcaccgg ctaactccgt gccagcagcc gcggtaatac 180
ggagggtgca agcgttaatc ggaattactg ggcgtaaagc gcacgcaggc ggtctgtcaa 240
gtcggatgtg aaatccccgg gctcaacctg ggaactgcat tcgaaactgg caggctagag 300
tcttgtagag gggggtagaa ttccaggtgt agcggtgaaa tgcgtagaga tctggaggaa 360
taccggtggc gaaggcggcc ccctggacaa agactgacgc tcaggtgcga aagcgtgggg 420
agcaaacagg 430
<210> 7
<211> 430
<212> DNA
<213>enteric microorganism (Sutterella)
<400> 7
gtggggaatt ttggacaatg ggggcaaccc tgatccagcc atgccgcgtg caggatgaag 60
gtcttcggat tgtaaactgc ttttgtcagg gacgaaaagg gatgcgataa caccgcattc 120
cgctgacggt acctgaagaa taagcaccgg ctaactacgt gccagcagcc gcggtaatac 180
gtagggtgca agcgttaatc ggaattactg ggcgtaaagc gtgcgcaggc ggttctgtaa 240
gatagatgtg aaatccccgg gctcaacctg ggaattgcat atatgactgc aggacttgag 300
tttgtcagag gagggtggaa ttccacgtgt agcagtgaaa tgcgtagata tgtggaagaa 360
caccgatggc gaaggcagcc ctctgggaca tgactgacgc tcatgcacga aagcgtgggg 420
agcaaacagg 430
<210> 8
<211> 405
<212> DNA
<213>enteric microorganism (achnospiracea_incertae_sedis)
<400> 8
gtggggaata ttgcacaatg gaggaaactc tgatgcagcg acgccgcgtg agtgaagaag 60
tatttcggta tgtaaagctc tatcagcagg gaagacagtg acggtacctg actaagaagc 120
tccggctaaa tacgtgccag cagccgcggt aatacgtatg gagcaagcgt tatccggatt 180
tactgggtgt aaagggagtg taggtggtat cacaagtcag aagtgaaagc ccggggctca 240
accccgggac tgcttttgaa actgtggaac tggagtgcag gagaggtaag tggaattcct 300
agtgtagcgg tgaaatgcgt agatattagg aggaacacca gtggcgaagg cggcttactg 360
gactgtaact gacactgagg ctcgaaagcg tggggagcaa acagg 405
<210> 9
<211> 406
<212> DNA
<213>enteric microorganism (Collinsella)
<400> 9
gtggggaatc ttgcgcaatg gggggaaccc tgacgcagcg acgccgcgtg cgggacggag 60
gccttcgggt cgtaaaccgc tttcagcagg gaagagtcaa gactgtacct gcagaagaag 120
ccccggctaa ctacgtgcca gcagccgcgg taatacgtag ggggcgagcg ttatccggat 180
tcattgggcg taaagcgcgc gtaggcggcc cggcaggccg ggggtcgaag cggggggctc 240
aaccccccga agcccccgga acctccgcgg cttgggtccg gtaggggagg gtggaacacc 300
cggtgtagcg gtggaatgcg cagatatcgg gtggaacacc ggtggcgaag gcggccctct 360
gggccgagac cgacgctgag gcgcgaaagc tgggggagcg aacagg 406
<210> 10
<211> 426
<212> DNA
<213>enteric microorganism (Prevotella)
<400> 10
gtgaggaata ttggtcaatg gacgagagtc tgaaccagcc aagtagcgtg caggatgacg 60
gccctatggg ttgtaaactg cttttgcgcg gggataacac cctccacgtg ctggaggtct 120
gcaggtaccg cgcgaataag gaccggctaa ttccgtgcca gcagccgcgg taatacggaa 180
ggtccgggcg ttatccggat ttattgggtt taaagggagc gtaggccgtg aggtaagcgt 240
gttgtgaaat gtaggcgccc aacgtctgca ctgcagcgcg aactgcccca cttgagtgcg 300
cgcaacgccg gcggaactcg tcgtgtagcg gtgaaatgct tagatatgac gaagaacccc 360
gattgcgaag gcagctggcg ggagcgtaac tgacgctgaa gctcgaaagc gcgggtatcg 420
aacagg 426
<210> 11
<211> 405
<212> DNA
<213>enteric microorganism (Blautia)
<400> 11
gtggggaata ttgcacaatg ggggaaaccc tgatgcagcg acgccgcgtg aaggaagaag 60
tatctcggta tgtaaacttc tatcagcagg gaagaaaatg acggtacctg actaagaagc 120
cccggctaac tacgtgccag cagccgcggt aatacgtagg gggcaagcgt tatccggatt 180
tactgggtgt aaagggagcg tagacggatg gacaagtctg atgtgaaagg ctggggctca 240
accccgggac tgcattggaa actgcccgtc ttgagtgccg gagaggtaag cggaattcct 300
agtgtagcgg tgaaatgcgt agatattagg aggaacacca gtggcgaagg cggcttactg 360
gacggtaact gacgttgagg ctcgaaagcg tggggagcaa acagg 405
<210> 12
<211> 408
<212> DNA
<213>enteric microorganism (Butyricicoccus)
<400> 12
gtggggaata ttgcgcaatg ggggaaaccc tgacgcagca acgccgcgtg attgaagaag 60
gccttcgggt tgtaaagatc tttaatcagg gacgaaacaa atgacggtac ctgaagaata 120
agctccggct aactacgtgc cagcagccgc ggtaatacgt agggagcaag cgttatccgg 180
atttactggg tgtaaagggc gcgcaggcgg gccggcaagt tggaagtgaa atctatgggc 240
ttaacccata aactgctttc aaaactgctg gtcttgagtg atggagaggc aggcggaatt 300
ccgtgtgtag cggtgaaatg cgtagatata cggaggaaca ccagtggcga aggcggcctg 360
ctggacatta actgacgctg aggcgcgaaa gcgtggggag caaacagg 408
<210> 13
<211> 430
<212> DNA
<213>enteric microorganism (Lactobacillus)
<400> 13
gtagggaatc ttccacaatg gacgcaagtc tgatggagca acgccgcgtg agtgaagaag 60
gtcttcggat cgtaaaactc tgttgttaga gaagaacacg agtgagagta actgttcatt 120
cgatgacggt atctaaccag caagtcacgg ctaactacgt gccagcagcc gcggtaatac 180
gtaggtggca agcgttgtcc ggatttattg ggcgtaaagg gaacgcaggc ggtcttttaa 240
gtctgatgtg aaagccttcg gcttaaccgg agtagtgcat tggaaactgg aagacttgag 300
tgcagaagag gagagtggaa ctccatgtgt agcggtgaaa tgcgtagata tatggaagaa 360
caccagtggc gaaagcggct ctctggtctg taactgacgc tgaggttcga aagcgtgggt 420
agcaaacagg 430
<210> 14
<211> 412
<212> DNA
<213>enteric microorganism (Bifidobacterium)
<400> 14
gtggggaata ttgcacaatg ggcgcaagcc tgatgcagcg acgccgcgtg agggatggag 60
gccttcgggt tgtaaacctc ttttgtttgg gagcaagcct tcgggtgagt gtacctttcg 120
aataagcgcc ggctaactac gtgccagcag ccgcggtaat acgtagggcg caagcgttat 180
ccggatttat tgggcgtaaa gggctcgtag gcggctcgtc gcgtccggtg tgaaagtcca 240
tcgcttaacg gtggatctgc gccgggtacg ggcgggctgg agtgcggtag gggagactgg 300
aattcccggt gtaacggtgg aatgtgtaga tatcgggaag aacaccgatg gcgaaggcag 360
gtctctgggc cgtcactgac gctgaggagc gaaagcgtgg ggagcgaaca gg 412
<210> 15
<211> 407
<212> DNA
<213>enteric microorganism (TM7_genera_incertae_sedis)
<400> 15
gtgaggaatc ttccacaatg ggcgaaagcc tgatggagca acgccgcgtg caggatgaag 60
gccttcgggt tgtaaactgc ttttataagt gaggaatatg acggtagctt atgaataagg 120
atcggctaac tacgtgccag cagccgcggt catacgtagg atccgagcgt tatccggagt 180
gactgggcgt aaagagttgc gtaggcggtt tgttaagtga atagtgaaat ctggtggctc 240
aaccatacag gctattattc aaactggcaa actcgagagt ggtagaggtc actggaattt 300
cttgtgtagg agtgaaatcc gtagatataa gaaggaacac cgatggcgta ggcaggtgac 360
tggaccattt ctgacgctaa ggcacgaaag cgtggggagc gaaccgg 407
<210> 16
<211> 407
<212> DNA
<213>enteric microorganism (Clostridiales)
<400> 16
gtggggaata ttgggcaatg ggggaaaccc tgacccagca acgccgcgtg aaggaagaag 60
gtcttcggat cgtaaacttc tatcctcggt gaagaggaga agacggtagc cgagaaggaa 120
gccccggcta actacgtgcc agcagccgcg gtaatacgta gggggcaagc gttgtccgga 180
atgattgggc gtaaagggcg tgtaggcggc taagtaagtc tggagtgaaa gtcctgcttt 240
taaggtggga attgctttgg atactgcata gctagagtgc aggagaggta agtggaattc 300
ccagtgtagc ggtgaaatgc gtagagattg ggaggaacac cagtggcgaa ggcgacttac 360
tggactgtaa ctgacgctga ggcgcgaaag tgtggggagc aaacagg 407
<210> 17
<211> 404
<212> DNA
<213>enteric microorganism (Ruminococcaceae)
<400> 17
gtgggggata ttgcacaatg gaggaaactc tgatgcagcg acgccgcgtg agggaagacg 60
gtcttcggat tgtaaacctc tgtctttggg gacgataatg acggtaccca aggaggaagc 120
tccggctaac tacgtgccag cagccgcggt aatacgtagg gagcgagcgt tgtccggaat 180
tactgggtgt aaagggagcg taggcggggt ctcaagtcga atgttaaatc taccggctca 240
actggtagct gcgttcgaaa ctggggctct tgagtgaagt agaggcaggc ggaattccta 300
gtgtagcggt gaaatgcgta gatattagga ggaacaccag tggcgaaggc ggcctgctgg 360
gcttttactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404
<210> 18
<211> 430
<212> DNA
<213>enteric microorganism (Acidaminococcus)
<400> 18
gtggggaatc ttccgcaatg gacgaaagtc tgacggagca acgccgcgtg agtgatgaag 60
gccttcgggt tgtaaaactc tgttgtcagg gacgaacgtg ctgatttaca atacacttca 120
gcagtgacgg tacctgacga ggaagccacg gctaactacg tgccagcagc cgcggtaata 180
cgtaggtggc aagcgttgtc cggaattatt gggcgtaaag agcatgtagg cgggctttta 240
agtccgacgt gaaaatgcgg ggcttaaccc cgtatggcgt tggatactgg aagtcttgag 300
tgcaggagag gaaaggggaa ttcccagtgt agcggtgaaa tgcgtagata ttgggaggaa 360
caccagtggc gaaggcgcct ttctggactg tgtctgacgc tgagatgcga aagccagggt 420
agcaaacggg 430
<210> 19
<211> 408
<212> DNA
<213>enteric microorganism (Fusobacterium)
<400> 19
gtggggaata ttggacaatg gaccaaaagt ctgatccagc aattctgtgt gcacgatgac 60
gtttttcgga atgtaaagtg ctttcagttg ggaagaaaaa aatgacggta ccaacagaag 120
aagtgacggc taaatacgtg ccagcagccg cggtaatacg tatgtcacaa gcgttatccg 180
gatttattgg gcgtaaagcg cgtctaggtg gttatgtaag tctgatgtga aaatgcaggg 240
ctcaactctg tattgcgttg gaaactgcat gactagagta ctggagaggt aagcggaact 300
acaagtgtag aggtgaaatt cgtagatatt tgtaggaatg ccgatgggga agccagctta 360
ctggacagat actgacgcta aagcgcgaaa gcgtgggtag caaacagg 408
<210> 20
<211> 430
<212> DNA
<213>enteric microorganism (Aggregatibacter)
<400> 20
gtggggaata ttgcgcaatg ggggcaaccc tgacgcagcc atgccgcgtg aatgaagaag 60
gccttcgggt tgtaaagttc tttcggtgac gaggaaggcg tgatgtttaa taggcatcac 120
gattgacgtt aatcacagaa gaagcaccgg ctaactccgt gccagcagcc gcggtaatac 180
ggagggtgcg agcgttaatc ggaataactg ggcgtaaagg gcacgcaggc ggctatttaa 240
gtgaggtgtg aaatccccgg gcttaacctg ggaattgcat ttcagactgg gtagctagag 300
tactttaggg aggggtagaa ttccacgtgt agcggtgaaa tgcgtagaga tgtggaggaa 360
taccgaaggc gaaggcagcc ccttgggaat gtactgacgc tcatgtgcga aagcgtgggg 420
agcaaacagg 430
<210> 21
<211> 411
<212> DNA
<213>enteric microorganism (Rothia)
<400> 21
gtggggaata ttgcacaatg ggcgcaagcc tgatgcagcg acgccgcgtg agggatgacg 60
gccttcgggt tgtaaacctc tgttagcagg gaagaagaga gattgacggt acctgcagag 120
aaagcgccgg ctaactacgt gccagcagcc gcggtaatac gtagggcgcg agcgttgtcc 180
ggaattattg ggcgtaaaga gcttgtaggc ggtttgtcgc gtctgctgtg aaaggccgga 240
gcttaactcc gtgtattgca gtgggtacgg gcagactaga gtgcagtagg ggagactgga 300
attcctggtg tagcggtgga atgcgcagat atcaggagga acaccgatgg cgaaggcagg 360
tctctgggct gtaactgacg ctgagaagcg aaagcatggg gagcgaacag g 411
<210> 22
<211> 425
<212> DNA
<213>enteric microorganism (Alistipes)
<400> 22
gtgaggaata ttggtcaatg gccgcaagtc tgaaccagcc atgccgcgtg caggatgacg 60
gctctatgag ttgtaaactg cttttgtact agggtaaact cacctacgtg taggtgactg 120
aaagtatagt acgaataagg atcggctaac tccgtgccag cagccgcggt aatacggagg 180
attcaagcgt tatccggatt tattgggttt aaagggtgcg taggcggttt gataagttag 240
aggtgaaatg ttagggctca accctgaaac tgcctctaat actgttggac tagagagtag 300
ttgcggtagg cggaatgtat ggtgtagcgg tgaaatgctt agagatcata cagaacaccg 360
attgcgaagg cagcttacca aactatatct gacgttgagg cacgaaagcg tggggagcaa 420
acagg 425
<210> 23
<211> 405
<212> DNA
<213>enteric microorganism (OTU_664)
<400> 23
gtggggaatc ttgcgcaatg ggcgaaagcc tgacgcagcc atgccgcgtg aatgatgaag 60
gtcttaggat tgtaaaattc tttcaccggg gacgataatg acggtacccg gagaagaagc 120
cccggctaac ttcgtgccag cagccgcggt aatacgaagg gggctagcgt tgctcggaat 180
tactgggcgt aaagggcgcg taggcggatc gttaagtcag aggtgaaatc ccagggctca 240
accctggaac tgcctttgat actggcgatc ttgagtatga gagaggtatg tggaactccg 300
agtgtagagg tgaaattcgt agatattcgg aagaacacca gtggcgaagg cgacatactg 360
gctcattact gacgctgagg cgcgaaagcg tggggagcaa acagg 405
<210> 24
<211> 430
<212> DNA
<213>enteric microorganism (Megamonas)
<400> 24
gtggggaatc ttccgcaatg ggcgaaagcc tgacggagca acgccgcgtg aacgatgaag 60
gtcttaggat cgtaaagttc tgttgttagg gacgaagggc aagggttata atacagcctt 120
tgtttgacgg tacctaacga ggaagccacg gctaactacg tgccagcagc cgcggtaata 180
cgtaggcggc aagcgttgtc cggaattatt gggcgtaaag ggagcgcagg cgggaaacta 240
agcggatctt aaaagtgcgg ggctcaaccc cgtgatgggg tccgaactgg ttttcttgag 300
tgcaggagag gaaagcggaa ttcccagtgt agcggtgaaa tgcgtagata ttgggaagaa 360
caccagtggc gaaggcggct ttctggactg taactgacgc tgaagctcga aagtgcgggt 420
atcgaacagg 430
<210> 25
<211> 408
<212> DNA
<213>enteric microorganism (Ruminococcaceae)
<400> 25
gtggggaata ttgggcaatg ggcgaaagcc tgacccagca acgccgcgtg aaggaagaag 60
gccttcgggt tgtaaacttc ttttaccagg gacgaaggac gtgacggtac ctggagaaaa 120
agcaacggct aactacgtgc cagcagccgc ggtaatacgt aggttgcaag cgttgtccgg 180
atttactgag tgtaaagggc gtgtaggcgg agatgcaagt taggagtgaa atctgtgggc 240
tcaacccata aactgcttct aaaactgtat cccttgagta tcggagaggc aagcggaatt 300
cctagtgtag cggtgaaatg cgtagatatt aggaggaaca ccagtggcga aggcggcttg 360
ctggacgaca actgacgctg aggcgcgaaa gcgtggggag caaacagg 408
<210> 26
<211> 425
<212> DNA
<213>enteric microorganism (Prevotella)
<400> 26
gtgaggaata ttggtcaatg ggcgagagtc tgaaccagcc aagtagcgtg caggatgacg 60
gccctatggg ttgtaaactg cttttataag ggaataaagt gagctacgtg tagctttttg 120
catgtacctt atgaataagg accggctaat tccgtgccag cagccgcggt aatacggaag 180
gtccgggcgt tatccggatt tattgggttt aaagggagcg taggccgtct tataagcgtg 240
ttgtgaaatg tagatgctca acatctgcac tgcagcgcga actggtttcc ttgagtacgc 300
acaaagtggg cggaattcgt ggtgtagcgg tgaaatgctt agatatcacg aagaactccg 360
attgcgaagg cagctcactg gagcgcaact gacgctgaag ctcgaaagtg cgggtatcga 420
acagg 425
<210> 27
<211> 424
<212> DNA
<213>enteric microorganism (Paraprevotella)
<400> 27
gtgaggaata ttggtcaatg gacgagagtc tgaaccagcc aagtagcgtg caggatgacg 60
gccctatggg ttgtaaactg cttttataag ggaataaagt gagtctcgtg agactttttg 120
catgtacctt atgaataagg accggctaat tccgtgccag cagccgcggt aatacggaag 180
gtccgggcgt tatccggatt tattgggttt aaagggagcg taggcgggct tttaagtcag 240
cggtcaaatg tcgtggctca accatgtcaa gccgttgaaa ctgtaagcct tgagtctgca 300
cagggcacat ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga 360
ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420
cagg 424
<210> 28
<211> 430
<212> DNA
<213>enteric microorganism (Klebsiella)
<400> 28
gtggggaata ttgcacaatg ggcgcaagcc tgatgcagcc atgccgcgtg tgtgaagaag 60
gccttcgggt tgtaaagcac tttcagcggg gaggaaggcg gtgaggttaa taacctcacc 120
gattgacgtt acccgcagaa gaagcaccgg ctaactccgt gccagcagcc gcggtaatac 180
ggagggtgca agcgttaatc ggaattactg ggcgtaaagc gcacgcaggc ggtttgttaa 240
gtcagatgtg aaatccccgg gctcaacctg ggaactgcat tcgaaactgg caggctagag 300
tcttgtagag gggggtagaa ttccaggtgt agcggtgaaa tgcgtagaga tctggaggaa 360
taccggtggc gaaggcggcc ccctggacaa agactgacgc tcaggtgcga aagcgtgggg 420
agcaaacagg 430
<210> 29
<211> 408
<212> DNA
<213>enteric microorganism (Ruminococcaceae)
<400> 29
gtgggggata ttggtcaatg ggggaaaccc tgaaccagca atgccgcgtg agggaagaag 60
gtcttcggat tgtaaaccta agtagtcagg gacgaagaaa gtgacggtac ctgaagagta 120
agctccggct aactacgtgc cagcagccgc ggtaatacgt agggagcgag cgttgtccgg 180
atttactggg tgtaaagggt gcgtaggcgg gtcggcaagt cagatgtgaa ataccggggc 240
ttaactccgg ggctgcattt gaaactgttg atcttgagtg aagtagaggc aggcggaatt 300
cctagtgtag cggtgaaatg cgtagatatt aggaggaaca ccagtggcga aggcggcctg 360
ctgggcttta actgacgctg aggcaccaaa gcatggggag caaacagg 408
<210> 30
<211> 405
<212> DNA
<213>enteric microorganism (Faecalibacterium)
<400> 30
gtggggaata ttgcacaatg ggggaaaccc tgatgcagcg acgccgcgtg gaggaagaag 60
gtcttcggat tgtaaactcc tgttgttggg gaagataatg acggtaccca acaaggaagt 120
gacggctaac tacgtgccag cagccgcggt aaaacgtagg tcacaagcgt tgtccggaat 180
tactgggtgt aaagggagcg caggcgggaa gacaagttgg aagtgaaatc tatgggctca 240
acccataaac tgctttcaaa actgtttttc ttgagtagtg cagaggtagg cggaattccc 300
ggtgtagcgg tggaatgcgt agatatcggg aggaacacca gtggcgaagg cggcctgctg 360
tagcgcaact gacgctgaag ctcgaaagcg tgggtatcga acagg 405
<210> 31
<211> 19
<212> DNA
<213>artificial sequence (Artificial Sequence)
<400> 31
actcctacgg gaggcagca 19
<210> 32
<211> 20
<212> DNA
<213>artificial sequence (Artificial Sequence)
<400> 32
ggactachvg ggtwtctaat 20
Claims (10)
1. a kind of for distinguishing the enteric microorganism gene marker of liver cancer and non-liver cancer, it is characterised in that: by SEQ ID NO:
30 kinds of microbial gene compositions, the microorganism shown in 1-30 are enriched in enteron aisle.
2. it is a kind of for detecting the detection reagent of enteric microorganism gene marker described in claim 1, including it is used for right to examin
Benefit require 1 described in 30 kinds of microbial genes shown in SEQ ID NO:1-30 primer.
3. detection reagent according to claim 1, it is characterised in that: the primer sequence is SEQ ID NO:31-32.
4. application of the detection reagent described in claim 2 in preparation liver cancer and the differentiation detection kit of non-liver cancer, the inspection
Test agent is suitable for detecting enteric microorganism gene described in claim 1.
5. application according to claim 4, it is characterised in that: the detection kit is suitable for distinguishing liver cancer patient and non-
Liver cancer patient, wherein non-liver cancer patient includes liver cirrhosis patient and healthy population.
6. application according to claim 2, it is characterised in that: detection reagent detects the excrement of the object, with
Just determine whether the sample includes microbial gene described in claim 1, if can establish difference liver cancer and non-liver cancer
Microbial gene model.
7. application according to claim 6, it is characterised in that: by collecting the fecal sample into group objects, extract micro- life
Object total DNA completes the 16S rDNA Miseq sequencing of microbial DNA, detects whether that there are 30 kinds of micro- lifes described in claim 1
Object gene.
8. application according to claim 7, it is characterised in that: by collecting the fecal sample into group objects, extract micro- life
Object total DNA carries out the 16S rDNA Miseq sequencing of intestinal flora；Based on high-flux sequence data, liver is established in training set
The microorganism of cancer and non-liver cancer patient distinguish model, establish liver cancer illness rate (probability of disease, POD) and refer to
Number；POD index is verified in verifying its discrimination of centralized calculation；Further in the independent diagnostics collection from different geographical
Middle carry out individual authentication realizes universality of the microbial gene difference model in Chinese liver cancer crowd.
9. application according to claim 4, which is characterized in that specifically include:
(1) fecal sample into group objects is collected, enters group objects and includes 75 liver cancer patients and 105 non-liver cancer patients, according to
The standard method for extracting of DNA completes the extracting of microorganism total DNA in fecal sample, completes enteron aisle in Illumina MiSeq platform
The 16S rDNA high-flux sequence of flora works；
(2) high-flux sequence data are based on, in the training set of microorganism difference model, in 75 liver cancer and 105 non-liver cancers
Between patient, identified for the model most based on a Random Forest model by the algorithm of five times of cross validation
30 good microbial gene markers；
(3) 30 microbial gene markers are based on, the trouble of liver cancer is calculated by using the ratio of the decision tree generated at random
Sick rate (Probability of disease, POD) index；
(4) discrimination of the microorganism difference model between 75 liver cancer and 105 non-liver cancers reaches 80.64%, POD and refers to
Number is significantly raised in liver cancer patient, has significant difference (p=1.5x10-14) between two groups；
(5) it is concentrated in verifying, which distinguishes discrimination of the model between 30 early liver cancers and 56 normal healthy controls
Reach 76.80%；POD index is significantly raised in early liver cancer patient, has significant difference (p=2.2x10- between two groups
7)；
(6) it is concentrated in verifying, which distinguishes model in the difference energy between 45 advanced liver cancers and 56 normal healthy controls
Power reaches 80.40%；POD index is late significantly raised in liver cancer patient, has significant difference (p=2.3x10- between two groups
6)；
(7) it is concentrated in independent diagnostics, which distinguishes model right in 18 liver cancer patients and 56 health from Xinjiang
Discrimination according between reaches 79.20%；POD index is significantly raised in the liver cancer patient of Xinjiang region, has between two groups aobvious
It writes sex differernce (p=0.00021)；
(8) it is concentrated in independent diagnostics, which distinguishes model right in 80 liver cancer patients and 56 health from Zhengzhou
Discrimination according between is up to 81.70%；POD index is significantly raised in the liver cancer patient of Zhengzhou area, has between two groups aobvious
It writes sex differernce (p=1.6x10-11).
10. it is a kind of for distinguishing the kit of the enteric microorganism model of liver cancer and non-liver cancer, including for detecting claim 1
The primer of 30 kinds of microbial genes shown in the SEQ ID NO:1-30.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111549155A (en) * | 2020-05-13 | 2020-08-18 | 朱彧 | Analysis technology for predicting liver diseases through relative abundance of intestinal flora |
| CN112609024A (en) * | 2020-12-31 | 2021-04-06 | 郑州大学第一附属医院 | Intestinal microbial gene marker for noninvasive diagnosis of novel coronavirus infected patient and application of intestinal microbial gene marker |
| CN113308560A (en) * | 2020-12-31 | 2021-08-27 | 郑州大学第一附属医院 | Oral microbial gene marker for noninvasive diagnosis of novel coronavirus infected patient and application of oral microbial gene marker |
| CN113528659A (en) * | 2020-09-18 | 2021-10-22 | 深圳汇芯生物医疗科技有限公司 | Risk assessment device for kidney cancer and bladder cancer |
| CN113699260A (en) * | 2021-09-29 | 2021-11-26 | 复旦大学附属中山医院 | Application of intestinal flora abundance detection in hepatic fibrosis and liver cirrhosis |
| CN114703305A (en) * | 2022-04-11 | 2022-07-05 | 郑州大学第一附属医院 | Oral microbial gene marker for predicting neutralizing antibody level of new coronary pneumonia patient one year later and application thereof |
| CN114717342A (en) * | 2022-04-11 | 2022-07-08 | 郑州大学第一附属医院 | Gut microbial gene markers for predicting neutralizing antibody levels in patients with new coronary pneumonia one year later and their applications |
| CN118389661A (en) * | 2024-02-21 | 2024-07-26 | 华中科技大学同济医学院附属协和医院 | Non-invasive biomarker for ALD liver injury and application thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014126044A1 (en) * | 2013-02-12 | 2014-08-21 | 公益財団法人がん研究会 | Method for inspecting risk of carcinogenesis |
| CN104195146A (en) * | 2014-07-15 | 2014-12-10 | 浙江大学 | Microbial marker of liver cirrhosis, and application |
| CN108504739A (en) * | 2018-05-15 | 2018-09-07 | 浙江大学 | A kind of biomarker and its application for hepatocarcinoma early diagnosis |
-
2018
- 2018-11-01 CN CN201811297644.6A patent/CN109371147A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014126044A1 (en) * | 2013-02-12 | 2014-08-21 | 公益財団法人がん研究会 | Method for inspecting risk of carcinogenesis |
| CN104195146A (en) * | 2014-07-15 | 2014-12-10 | 浙江大学 | Microbial marker of liver cirrhosis, and application |
| CN108504739A (en) * | 2018-05-15 | 2018-09-07 | 浙江大学 | A kind of biomarker and its application for hepatocarcinoma early diagnosis |
Non-Patent Citations (2)
| Title |
|---|
| ZHIGANG REN ET AL.: "Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma", 《GUT MICROBIOTA》 * |
| 蒋建文等: "肠道微生态与肝癌关系研究新进展", 《传染病信息》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111549155A (en) * | 2020-05-13 | 2020-08-18 | 朱彧 | Analysis technology for predicting liver diseases through relative abundance of intestinal flora |
| CN113528659A (en) * | 2020-09-18 | 2021-10-22 | 深圳汇芯生物医疗科技有限公司 | Risk assessment device for kidney cancer and bladder cancer |
| CN112609024A (en) * | 2020-12-31 | 2021-04-06 | 郑州大学第一附属医院 | Intestinal microbial gene marker for noninvasive diagnosis of novel coronavirus infected patient and application of intestinal microbial gene marker |
| CN113308560A (en) * | 2020-12-31 | 2021-08-27 | 郑州大学第一附属医院 | Oral microbial gene marker for noninvasive diagnosis of novel coronavirus infected patient and application of oral microbial gene marker |
| CN112609024B (en) * | 2020-12-31 | 2023-08-15 | 郑州大学第一附属医院 | A gut microbial gene marker and its application for non-invasive diagnosis of patients with novel coronavirus infection |
| CN113699260A (en) * | 2021-09-29 | 2021-11-26 | 复旦大学附属中山医院 | Application of intestinal flora abundance detection in hepatic fibrosis and liver cirrhosis |
| CN114703305A (en) * | 2022-04-11 | 2022-07-05 | 郑州大学第一附属医院 | Oral microbial gene marker for predicting neutralizing antibody level of new coronary pneumonia patient one year later and application thereof |
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| CN114717342B (en) * | 2022-04-11 | 2023-07-07 | 郑州大学第一附属医院 | Intestinal microbial gene marker for predicting neutralizing antibody level of new coronal pneumonia patient after one year and application thereof |
| CN114703305B (en) * | 2022-04-11 | 2023-07-07 | 郑州大学第一附属医院 | Oral microbial gene markers and their applications for predicting neutralizing antibody levels in patients with new coronary pneumonia one year later |
| CN118389661A (en) * | 2024-02-21 | 2024-07-26 | 华中科技大学同济医学院附属协和医院 | Non-invasive biomarker for ALD liver injury and application thereof |
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