CN110914453B - Biomarkers for atherosclerotic cardiovascular disease - Google Patents
Biomarkers for atherosclerotic cardiovascular disease Download PDFInfo
- Publication number
- CN110914453B CN110914453B CN201780093309.1A CN201780093309A CN110914453B CN 110914453 B CN110914453 B CN 110914453B CN 201780093309 A CN201780093309 A CN 201780093309A CN 110914453 B CN110914453 B CN 110914453B
- Authority
- CN
- China
- Prior art keywords
- cardiovascular disease
- biomarker
- atherosclerotic cardiovascular
- acvd
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 201000001320 Atherosclerosis Diseases 0.000 title claims abstract description 77
- 206010003210 Arteriosclerosis Diseases 0.000 title claims abstract description 74
- 239000000090 biomarker Substances 0.000 title claims abstract description 50
- 230000002550 fecal effect Effects 0.000 claims description 9
- 238000012163 sequencing technique Methods 0.000 claims description 6
- 239000003153 chemical reaction reagent Substances 0.000 claims description 4
- 238000000034 method Methods 0.000 abstract description 15
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 12
- 108090000623 proteins and genes Proteins 0.000 description 27
- 239000000523 sample Substances 0.000 description 17
- 238000012360 testing method Methods 0.000 description 14
- 238000012549 training Methods 0.000 description 13
- 108020004414 DNA Proteins 0.000 description 11
- 244000005709 gut microbiome Species 0.000 description 8
- 230000000968 intestinal effect Effects 0.000 description 8
- 201000010099 disease Diseases 0.000 description 7
- 208000029078 coronary artery disease Diseases 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000002586 coronary angiography Methods 0.000 description 4
- 230000000813 microbial effect Effects 0.000 description 4
- 238000003752 polymerase chain reaction Methods 0.000 description 4
- 238000013179 statistical model Methods 0.000 description 4
- 108091028043 Nucleic acid sequence Proteins 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000011109 contamination Methods 0.000 description 3
- 238000002790 cross-validation Methods 0.000 description 3
- 208000035475 disorder Diseases 0.000 description 3
- 230000002068 genetic effect Effects 0.000 description 3
- PHIQHXFUZVPYII-ZCFIWIBFSA-N (R)-carnitine Chemical compound C[N+](C)(C)C[C@H](O)CC([O-])=O PHIQHXFUZVPYII-ZCFIWIBFSA-N 0.000 description 2
- 101150090724 3 gene Proteins 0.000 description 2
- 108700005443 Microbial Genes Proteins 0.000 description 2
- 208000005764 Peripheral Arterial Disease Diseases 0.000 description 2
- 208000030831 Peripheral arterial occlusive disease Diseases 0.000 description 2
- 208000001647 Renal Insufficiency Diseases 0.000 description 2
- 206010000891 acute myocardial infarction Diseases 0.000 description 2
- 125000003275 alpha amino acid group Chemical group 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 210000003608 fece Anatomy 0.000 description 2
- 238000009396 hybridization Methods 0.000 description 2
- 208000015181 infectious disease Diseases 0.000 description 2
- 201000006370 kidney failure Diseases 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000006371 metabolic abnormality Effects 0.000 description 2
- 208000030159 metabolic disease Diseases 0.000 description 2
- 244000005700 microbiome Species 0.000 description 2
- 208000033808 peripheral neuropathy Diseases 0.000 description 2
- 238000007637 random forest analysis Methods 0.000 description 2
- 235000020989 red meat Nutrition 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012552 review Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000002560 therapeutic procedure Methods 0.000 description 2
- 206010002388 Angina unstable Diseases 0.000 description 1
- 206010065558 Aortic arteriosclerosis Diseases 0.000 description 1
- 208000027796 Blood pressure disease Diseases 0.000 description 1
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 1
- 208000031229 Cardiomyopathies Diseases 0.000 description 1
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 208000035473 Communicable disease Diseases 0.000 description 1
- 102000053602 DNA Human genes 0.000 description 1
- 208000027244 Dysbiosis Diseases 0.000 description 1
- 206010019663 Hepatic failure Diseases 0.000 description 1
- 241000282412 Homo Species 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
- 238000000585 Mann–Whitney U test Methods 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 208000008589 Obesity Diseases 0.000 description 1
- 208000031481 Pathologic Constriction Diseases 0.000 description 1
- 102000006382 Ribonucleases Human genes 0.000 description 1
- 108010083644 Ribonucleases Proteins 0.000 description 1
- 208000007718 Stable Angina Diseases 0.000 description 1
- 208000006011 Stroke Diseases 0.000 description 1
- 208000007814 Unstable Angina Diseases 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000002583 angiography Methods 0.000 description 1
- 201000001962 aortic atherosclerosis Diseases 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 238000003149 assay kit Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000003115 biocidal effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 210000004204 blood vessel Anatomy 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 235000011089 carbon dioxide Nutrition 0.000 description 1
- 208000026106 cerebrovascular disease Diseases 0.000 description 1
- 235000012000 cholesterol Nutrition 0.000 description 1
- 208000037976 chronic inflammation Diseases 0.000 description 1
- 230000006020 chronic inflammation Effects 0.000 description 1
- 238000010968 computed tomography angiography Methods 0.000 description 1
- 239000002872 contrast media Substances 0.000 description 1
- 238000004925 denaturation Methods 0.000 description 1
- 230000036425 denaturation Effects 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 230000003831 deregulation Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 206010012601 diabetes mellitus Diseases 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 230000002526 effect on cardiovascular system Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000001502 gel electrophoresis Methods 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012165 high-throughput sequencing Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 210000000987 immune system Anatomy 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 201000004332 intermediate coronary syndrome Diseases 0.000 description 1
- 238000011901 isothermal amplification Methods 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 210000004185 liver Anatomy 0.000 description 1
- 208000007903 liver failure Diseases 0.000 description 1
- 231100000835 liver failure Toxicity 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 239000002207 metabolite Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002107 myocardial effect Effects 0.000 description 1
- 208000010125 myocardial infarction Diseases 0.000 description 1
- 231100000957 no side effect Toxicity 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 235000020824 obesity Nutrition 0.000 description 1
- 230000000414 obstructive effect Effects 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 244000052769 pathogen Species 0.000 description 1
- 230000008506 pathogenesis Effects 0.000 description 1
- 230000001717 pathogenic effect Effects 0.000 description 1
- 230000001991 pathophysiological effect Effects 0.000 description 1
- 230000037361 pathway Effects 0.000 description 1
- 230000010412 perfusion Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- WTJKGGKOPKCXLL-RRHRGVEJSA-N phosphatidylcholine Chemical compound CCCCCCCCCCCCCCCC(=O)OC[C@H](COP([O-])(=O)OCC[N+](C)(C)C)OC(=O)CCCCCCCC=CCCCCCCCC WTJKGGKOPKCXLL-RRHRGVEJSA-N 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 239000002096 quantum dot Substances 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000000344 soap Substances 0.000 description 1
- 230000036262 stenosis Effects 0.000 description 1
- 208000037804 stenosis Diseases 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 230000009885 systemic effect Effects 0.000 description 1
- 238000009966 trimming Methods 0.000 description 1
- 208000001072 type 2 diabetes mellitus Diseases 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
- C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/158—Expression markers
Landscapes
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Organic Chemistry (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Engineering & Computer Science (AREA)
- Genetics & Genomics (AREA)
- Biotechnology (AREA)
- Immunology (AREA)
- Microbiology (AREA)
- Molecular Biology (AREA)
- Physics & Mathematics (AREA)
- Biophysics (AREA)
- Biochemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
Use of a biomarker in predicting the risk of an atherosclerotic cardiovascular disease or a related disorder thereof and a method of predicting the risk of an atherosclerotic cardiovascular disease or a related disorder thereof.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of biological detection, and more particularly, to biomarkers, use of the biomarkers in predicting the risk of an atherosclerotic cardiovascular disease or related condition thereof, methods of predicting the risk of an atherosclerotic cardiovascular disease or related condition thereof, and kits for predicting the risk of an atherosclerotic cardiovascular disease or related condition thereof.
Background
Atherosclerotic cardiovascular disease (ACVD) is generally caused by the build-up of plaque on the arterial wall, especially on the large and medium-sized arteries serving the heart (i.e., atherosclerosis). It refers to the following conditions: coronary Heart Disease (CHD), cerebrovascular disease, peripheral arterial disease, and aortic atherosclerosis. These conditions have similar causes, mechanisms and methods of treatment.
The "gold standard" for detecting ACVD is invasive coronary angiography. However, this is expensive and may present a risk to the patient. Prior to angiography, non-invasive diagnostic modalities such as Myocardial Perfusion Imaging (MPI) and CT angiography can be used, but they have complications including radiation exposure, contrast agent sensitivity, and only moderately increase the recognition rate of obstructive ACVD.
Prior knowledge suggests that genetic factors, environmental factors, and interactions thereof, together induce complex phenotypes and many diseases. In recent years, GWAS (whole genome association research) has conducted an increasing study of ACVD and revealed 10.6% of endogenous etiologies caused by 46 common variants (ehset, g.b. et al Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478,103-109, incorporated herein by reference). However, we still need to further understand the contribution of genes to disease.
Our "forgetting organs", the intestinal microbiota, plays a vital role in our health in many ways, such as harvesting energy from food, producing important metabolites, promoting development and maturation of the immune system, and protecting the host from pathogen infection, etc. Recent studies have shown the presence of dysbacterioses, chronic inflammation and metabolic abnormalities in the gut of certain metabolic diseases (e.g. diabetes, obesity and coronary artery disease). A recent study showed that intestinal microorganisms can metabolize red meat component (L-carnitine, phosphatidylcholine, cholesterol) to TMA, which is then further oxidized to TMA O in the liver, causing oxidation reactions in the blood vessels, leading to inflammation and lipid deposition, ultimately leading to atherosclerosis and coronary heart disease (Koeth, R.A. et al Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, proteins atherosclerosis. Nature medium 19,576-585, incorporated herein by reference). These studies indicate that deregulation of the intestinal microbial content can strongly influence the pathogenesis of ACVD by inducing metabolic abnormalities in humans. However, the lack of a large study cohort for the macrogenomic characterization of this ACVD main population prevents further studies of the effects exerted by microbiomes.
Disclosure of Invention
Embodiments of the present disclosure seek to at least somewhat address at least one of the problems existing in the prior art, or to provide consumers with a useful commercial choice.
Embodiments of the first broad aspect of the present disclosure provide a biomarker, wherein the biomarker comprises the amino acid sequence as set forth in SEQ ID NO: 1-3.
ATGAATTGTGAAACTGTTGCCCTCGGGTCGTTTTGCAAGTTGAAGTCGAGGAGAGGAAAAAAACAAAAAGGAGAAATACTCATGGCAGTAATTTCAATGAAACAACTTCTTGAGGCTGGTGTACACTTTGGTCACCAAACTCGTCGCTGGAACCCTAAGATGGCTAAGTACATCTTCACTGAGCGTAACGGAATCCACGTTATCGACTTGCAACAAACTGTAAAATATGCTGACCAAGCTTACGATTTCATGCGTGATGCAGCAGCTAACGATGCAGTTGTATTGTTCGTTGGTACTAAGAAACAAGCCGCAGATGCAGTTGCTGAAGAAGCTGTTCGTGCAGGACAATACTTCATCAACCACCGTTGGTTGGGTGGAACTCTTACTAACTGGGGAACTATCCAAAAACGTATCGCTCGTTTGAAAGAAATCAAACGTATGGAAGAAGAAGGAATCTTCGACGTTCTTCCTAAGAAAGAAGTTGCACTTCTTAATAAACAGCGTGCACGTCTTGAAAAATTCTTGGGTGGTATCGAAGACATGCCTCGTATCCCAGATGTAATGTACGTAGTTGACCCACATAAAGAGCAAATCGCTGTTAAAGAAGCTAAAAAATTGGGTATCCCAGTTGTAGCGATGGTTGACACAAACACTGATCCAGACGATATCGATGTAATCATCCCAGCTAACGATGACGCTATCCGCGCGGTTAAACTGATCACAGCTAAATTGGCTGACGCTATCATCGAAGGACGTCAAGGTGAAGATGCAACAGCAGTTGAAGCAGAATTTGCAGCTTCAGAAGCTCAAGCAGACTCAATCGAAGAAATCGTTGAAGTTGTAGAAGGCGACAACGCTTAA(SEQ ID NO:1).
TTGGCAATTAACGCACAAGAAATCAGCGCTTTAATTAAGCAACAAATTGAAAATTTCAAACCCAATTTTGATGTGACTGAAACAGGTGTTGTAACCTATATCGGGGACGGTATCGCGCGTGCTCACGGCCTTGAAAATGCCATGAGTGGAGAGTTGTTGATTTTTGAAAACGGCTCTTATGGTATGGCTCAAAACTTGGAGTCAACAGATGTTGGTATTATCATCCTAGGTGACTTTACAGATATCCGTGAAGGTGATACAATTCGCCGTACAGGTAAAATCATGGAAGTCCCAATAGGTGAAAGTCTGATTGGTCGTGTTGTGGATCCTCTTGGTCGTCCAGTTGACGGTCTTGGAGAAATCCACACTGATAAAACTCGTCCAGTTGAAGCGCCAGCTCCTGGTGTTATGCAACGTAAGTCTGTATCAGAACCATTGCAAACTGGTTTGAAAGCTATCGACGCCCTTGTACCGATTGGTCGTGGTCAACGTGAGTTGATTATCGGTGACCGTCAGACAGGGAAAACAACTATTGCGATTGATACAATCTTGAACCAAAAAGGTCAAGATATGATCTGTATTTATGTCGCTATTGGACAAAAAGAATCAACAGTTCGTACGCAAGTAGAAACACTACGTCAGTACGGTGCCTTGGACTACACAATCGTTGTGACTGCCTCTGCTTCACAACCATCTCCATTGCTTTTCCTAGCTCCTTATGCTGGGGTTGCCATGGCGGAAGAATTTATGTACCAAGGCAAGCATGTTTTGATCGTTTATGATGATCTTTCAAAACAAGCGGTCGCTTATCGTGAACTTTCTCTCTTGCTTCGTCGTCCACCAGGTCGTGAAGCCTTCCCAGGGGATGTTTTCTACCTTCACAGCCGTTTGCTTGAGCGC(SEQ ID NO:2).
AAGGCACTCGAGGGAGCGCTCCGCGACCTCACCGCGATCACCGGCCAGAAGCCCGTGACCACCCGCGCCAAGAAGTCCATCGCGCAGTTCAAGCTGCGTGAGGGCCAGGCGATCGGTGCGCACGTCACGCTTCGCGGCGACCGCATGTGGGAGTTCCTGGATCGCCTCCTCTCGACGGCGCTTCCCCGTATCCGTGACTTCCGCGGACTGTCCTCCAAGCAGTTCGACGGTCACGGCAATTACACCTTCGGTCTCACGGAACAGTCGATGTTCCACGAGATCGATCAGGACTCGATCGACCGAGTGCGCGGCATGGACATCACGGTTGTCACCTCGGCCACCACCGACGAAGAGGGTCGCGCCCTTCTCCGTCACCTCGGCTTCCCGTTCAAGGAGGACTGA(SEQ ID NO:3)。
The inventors have surprisingly found that genes in the gut microbiota described above can be effectively used as biomarkers for predicting the risk of atherosclerotic cardiovascular disease or related conditions thereof.
Embodiments of the second broad aspect of the present disclosure provide the use of the above biomarker in predicting the risk of an atherosclerotic cardiovascular disease or associated condition thereof.
Embodiments of the third broad aspect of the present disclosure provide a method of predicting the risk of an atherosclerotic cardiovascular disease or associated condition thereof. According to an embodiment of the invention, the method comprises: (1) Determining the relative abundance of the above-described biomarker in a sample of a test subject; (2) Predicting the risk of an atherosclerotic cardiovascular disease or a related condition thereof based on the relative abundance of the biomarker. The above method can effectively predict the risk of atherosclerotic cardiovascular disease or its related condition. The reliability of the prediction result is high.
Embodiments of the fourth broad aspect of the present disclosure provide a kit for predicting the risk of an atherosclerotic cardiovascular disease or associated condition thereof. According to an embodiment of the invention, a kit comprises: a reagent configured to detect the biomarker. The kit finds use in effectively and easily predicting the risk of atherosclerotic cardiovascular disease or a related condition thereof.
The above summary of the present disclosure is not intended to describe each disclosed embodiment or every implementation of the present disclosure. The figures and the detailed description that follow more particularly exemplify illustrative embodiments.
Additional aspects and advantages of embodiments of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of embodiments of the disclosure.
Detailed Description
Reference will be made in detail to embodiments of the present disclosure. The embodiments described herein with reference to the drawings are illustrative, exemplary, and serve to generally understand the disclosure. The embodiments should not be construed as limiting the disclosure. Throughout the specification, identical or similar elements and elements having identical or similar functions are denoted by similar reference numerals.
Biomarkers
Embodiments of the first broad aspect of the present disclosure provide a biomarker, wherein the biomarker comprises the amino acid sequence as set forth in SEQ ID NO: 1-3. The inventors have surprisingly found that genes in the gut microbiota described above can be effectively used as biomarkers for predicting the risk of atherosclerotic cardiovascular disease or related conditions thereof.
The assessment and characterization of the intestinal microbial content has become a major area of research in human diseases including ACVD. To analyze the intestinal microbial content in ACVD, the inventors performed a protocol of metagenomic association analysis (MGWAS) (Qin, j. Et al a methonom-wide association study of gut microbiota in type 2 diabetes.Nature 490,55-60 (2012), incorporated herein by reference) based on deep shotgun sequencing of intestinal microbial DNA from 405 chinese individuals (n=218 ACVD,187 healthy controls; table 1). The present inventors identified and validated 3 gene markers associated with ACVD. To explore the diagnostic value of fecal microbial genes for ACVD, the inventors constructed random forest classifiers from 405 ACVD and control samples, performed 5-fold cross-validation of 5 replicates, and identified 3 optimized diseases associated with intestinal microbial gene markers. The present inventors' data provide insight into the characteristics of intestinal metagenome associated with risk of ACVD, examples of future studies of pathophysiological roles of intestinal metagenome in other related diseases, and the usefulness of gut flora-based methods for assessing individuals at risk of such disorders.
Use of biomarkers
Embodiments of the second broad aspect of the present disclosure provide the use of the above biomarker in predicting the risk of an atherosclerotic cardiovascular disease or associated condition thereof.
Method for predicting ACVD risk
Embodiments of the third broad aspect of the present disclosure provide a method of predicting the risk of an atherosclerotic cardiovascular disease or associated condition thereof. According to an embodiment of the invention, the method comprises: (1) Determining the relative abundance of the above-described biomarker in a sample of a test subject; and (2) predicting the risk of atherosclerotic cardiovascular disease or a related condition thereof based on the relative abundance of the above-described biomarkers. The above method can effectively predict the risk of atherosclerotic cardiovascular disease or its related condition. The reliability of the prediction result is high.
According to an embodiment of the disclosure, wherein the relative abundance of the biomarker is obtained by sequencing.
According to an embodiment of the disclosure, step (2) comprises comparing the relative abundance of the biomarker to a preset threshold. In some embodiments of the present disclosure, a sufficient difference between the greater relative abundance and the preset threshold may be indicative of the subject being tested having or at risk of developing an atherosclerotic cardiovascular disease or related condition thereof. As will be appreciated by those skilled in the art, values of "sufficient difference" and "preset value" may be obtained based on known conditions having an atherosclerotic cardiovascular disease or related disorder from some control samples. And according to another embodiment of the present disclosure, the preset threshold is about at least 0.5.
It should be noted that the relative abundance is a true value or probability of relative abundance, where the probability is a probability of atherosclerotic cardiovascular disease by comparing the relative abundance information of the biomarkers in the subject sample to the training dataset using a multivariate statistical model.
According to an embodiment of the disclosure, wherein the sample is an intestinal microbiota, optionally obtained from faeces of a test subject. The intestinal microbiota is obtained from fecal samples of ACVD patients, which is low cost, safe and has no side effects. Fecal analysis can ensure accuracy, safety, affordability, and patient compliance. And the fecal sample is transportable. Specific biomarkers can be used as non-invasive tests for early diagnosis of patients suffering from ACVD, thereby extending survival and improving quality of life.
According to embodiments of the present disclosure, the above-described method may be implemented as follows: (1) Determining the relative abundance of the biomarker in a sample of the test subject; (2) Comparing the relative abundance information of the biomarkers in the subject sample to a training dataset by using a multivariate statistical model, thereby obtaining a probability of atherosclerotic cardiovascular disease; wherein a probability of the atherosclerotic cardiovascular disease being greater than a threshold value indicates that the subject being tested has or is at risk of developing an atherosclerotic cardiovascular disease or associated condition thereof. A training dataset is constructed based on the relative abundance information of each biomarker for a plurality of subjects with ACVD and a plurality of normal subjects using a multivariate statistical model, optionally the multivariate statistical model is a random forest model. The training dataset is a matrix, each row represents each biomarker of the biomarker set described above, each column represents a sample, each cell represents a plot of the relative abundance of the biomarker in the sample, the sample disease state is a vector, 1 is ACVD, and 0 is a control. The relative abundance information for each of SEQ ID NOs 1, 2 and 3 is the relative abundance information shown in table 4. A probability of ACVD of at least 0.5 indicates that the subject being tested has or is at risk of developing ACVD or a related disorder.
Kit for predicting ACVD risk
Embodiments of the fourth broad aspect of the present disclosure provide a kit for predicting the risk of an atherosclerotic cardiovascular disease or associated condition thereof. According to an embodiment of the invention, a kit comprises: a reagent configured to detect the biomarker. The kit finds use in effectively and easily predicting the risk of atherosclerotic cardiovascular disease or a related condition thereof.
According to an embodiment of the present disclosure, wherein the reagent comprises at least one of a probe, a primer, a gene chip, and an antibody. Probes, gene chips and antibodies specifically recognize the above biomarkers, and primers specifically amplify the above biomarkers based on Polymerase Chain Reaction (PCR) assays. The kit is comfortable and non-invasive compared to invasive coronary angiography, so one can more easily participate in a given screening procedure.
It is believed that the 3 ACVD-related gene markers of the intestinal microbiota are of great value for enhancing the early discovery of metabolic diseases for the following reasons. First, the markers of the present invention are more specific and sensitive than conventional markers. Second, stool analysis can ensure accuracy, safety, affordability, and patient compliance. And the fecal sample is transportable. Polymerase Chain Reaction (PCR) based detection methods are both comfortable and non-invasive compared to invasive coronary angiography, and therefore one would be more likely to participate in a given screening procedure. Third, the markers of the invention can also be used as tools for therapy monitoring of ACVD patients to detect response to therapy.
Examples
Identification of 3 biomarkers to assess risk of ACVD
Sample collection
Samples of 405 subjects, including 218 individuals with ACVD and 187 control subjects, were collected at the general hospital medical study center in guangdong province. Individuals with ACVD showed clinical manifestations of stable angina, unstable angina or Acute Myocardial Infarction (AMI) (table 1). ACVD diagnosis was confirmed by coronary angiography and individuals with > 50% stenosis in single or multiple vessels were accepted. All patients were chinese han nationality, had no blood relationship, and were between 40 and 80 years of age. Exclusion criteria included persistent infectious disease, cancer, renal or hepatic failure, peripheral neuropathy, stroke, and antibiotic usage within one month of sample collection. At physical examination, no clinically significant ACVD symptoms were present in all enrolled healthy control individuals. Demographic data and cardiovascular risk factors are collected by questionnaires. Individuals with peripheral arterial disease, known coronary artery disease or myocardial infarction, cardiomyopathy, renal failure, peripheral neuropathy, systemic disease, and stroke are excluded. Fresh feces from each subject were collected the first morning after admission and frozen on dry ice for 30 minutes, stored in a-80 ℃ freezer, and then further analyzed.
The study was approved by the ethical review Committee of general Hospital medicine, guangdong province and the ethical review Committee of Shenzhen Daliving science, inc. Informed consent was obtained from all participants.
Table 1: baseline characteristics of ACVD cases and controls. Both age and BMI were tested by Wilcoxon rank sum test, while gender was tested by Fisher test.
| Parameters (parameters) | Case (n=218) | Case NA | Control (n=187) | Control NA | P value |
| Age of | 60.8 | 0 | 60.2 | 7 | 0.094 |
| Sex (M: F) | 161:53 | 4 | 75:111 | 1 | 1.263e-12 |
| BMI | 24.54 | 67 | 24.41 | 7 | 0.0289 |
Note that: the third and fifth columns are the number of subjects who do not know age, gender, or BMI information.
Extraction of DNA from fecal samples
Fecal samples were thawed on ice and DNA extracted using Qiagen QIAamp DNA Stool Mini Kit (Qiagen) according to manufacturer's instructions. The extract was treated with DNase-free RNase to eliminate RNA contamination. The amount of DNA was determined using a NanoDrop spectrophotometer, qubit Fluorometer (with Quant-iTTM dsDNA BR assay kit) and gel electrophoresis.
DNA library construction and sequencing of fecal samples
The construction of the DNA library was performed according to the manufacturer's instructions (Illumina). We used the same workflow 5 as described previously to perform cluster generation, template hybridization, isothermal amplification, linearization, blocking and denaturation, hybridization of sequencing primers. We constructed a double-ended (PE) library with insert sizes of 350bp for each sample, followed by high-throughput sequencing to obtain about 3000 ten thousand PE reads of 2x100bp in length. Low quality reads with indeterminate "N" bases, linker contamination and human DNA contamination are filtered from Illumina original reads, and high quality reads are obtained by simultaneously trimming the terminal bases of the low quality reads.
Metagenomic data processing and analysis
Construction of a genetic map. Using a standard with identity > 90%, high quality reads were aligned with 9,879,896 genes (9.9M gene set) by SOAP 2. Sequencing-based gene abundance patterns were performed as previously described (Li, J. Et al An integrated catalog of reference genes in the human gut Microbiol. Nat. Biotechnol.32,834-841 (2014), incorporated herein by reference).
Genes identified by MGWAS that are associated with ACVD. After filtering 9,879,896 genes at an incidence rate in more than 10 samples, the inventors identified potential biomarkers of ACVD from the remaining genes by MGWAS method using minimum redundancy maximum correlation (mRMR) feature selection method (H.Peng, F.Long, C.Ding, feature selection based on mutual information: criterion of max-dependency, max-reduction, and min-reduction, ieee transactions on pattern analysis and machine intelligence 27,1226, 8 months 2005, incorporated herein by reference), and they selected the first 500 most powerful whole genes.
They rank the 500 genes by training set cross-validation, test set ROC and prediction error, and finally select the 3 best genes (tables 2 and 3).
Considering AUC and estimated error rate for all 405 samples (see table 5 and table 6), the inventors selected the 3 most discriminatory genes consisting of genes 3050214, 2841974, 6560409 (see table 3).
The inventors tested the ability to isolate ACVD patients and controls using these 3 genes as biomarkers, respectively: they randomly split 405 samples into a training set (70% of 405 samples) and a test set (30% of 405 samples), then calculate the estimated error rate and AUC for each biomarker in the training set by cross-validation, and calculate the estimated error and AUC for each biomarker in the test set by a module built from the training set (tables 4, 5 and 6).
In addition, the inventors calculated the expected error rate and AUC from the 3 combined gene sets on the training and test sets (table 7), found that AUC for the training set was 0.796, AUC for the test set was 0.761, error rate in the training set was 0.278, and error rate in the test set was 0.322.
Table 2: the 3 most discriminating genes associated with ACVD
(enrichment sample: 1: ACVD,0: control)
Table 3: sequence numbers of 3 Gene markers
| Gene numbering | SEQ ID NO | Base factor (Length) | 9.9M Gene name in Gene set |
| 3050214 | 1 | 861 | N056A_GL0000096 |
| 2841974 | 2 | 900 | ED9A_GL0080283 |
| 6560409 | 3 | 402 | NLM027_GL0004265 |
/>
Table 5 probability of ACVD calculated from 3 genes on each of 405 samples (enriched samples: 1: ACVD;0: control, if probability. Gtoreq.0.5, subject at risk of ACVD) respectively
/>
/>
/>
/>
/>
/>
/>
/>
/>
/>
/>
/>
Table 6: the probability of ACVD calculated from each of the 3 genes on 405 samples (70% of 405 samples are training sets and 30% of 405 samples are test sets)
/>
Table 7: the probability of ACVD calculated by 3 genome aggregation on 405 samples (70% of 405 samples are training sets and 30% of 405 samples are test sets)
In addition, terms such as "first" and "second" are used herein for descriptive purposes and not to indicate or imply relative importance or significance.
Reference in the specification to "an embodiment," "some embodiments," "one embodiment," "another example," "an example," "a particular example," or "some examples" means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. Thus, phrases such as "in some embodiments," "in one embodiment," "in an embodiment," "in another example," "in an example," "in a particular example," or "in certain examples" appearing in various places throughout the specification are not necessarily referring to the same implementation or example of the disclosure. Furthermore, the particular features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.
Although illustrative embodiments have been shown and described, it will be understood by those skilled in the art that the above embodiments are not to be construed as limiting the present disclosure and that changes, substitutions and modifications may be made to the embodiments without departing from the spirit, principles and scope of the disclosure.
Sequence listing
<110> BGI SHENZHEN
<120> biomarkers for atherosclerotic cardiovascular disease
<130> PIOC3172755P
<160> 3
<170> patent in version 3.3
<210> 1
<211> 861
<212> DNA
<213> artificial sequence
<220>
<223> biomarker nucleotide sequences
<400> 1
atgaattgtg aaactgttgc cctcgggtcg ttttgcaagt tgaagtcgag gagaggaaaa 60
aaacaaaaag gagaaatact catggcagta atttcaatga aacaacttct tgaggctggt 120
gtacactttg gtcaccaaac tcgtcgctgg aaccctaaga tggctaagta catcttcact 180
gagcgtaacg gaatccacgt tatcgacttg caacaaactg taaaatatgc tgaccaagct 240
tacgatttca tgcgtgatgc agcagctaac gatgcagttg tattgttcgt tggtactaag 300
aaacaagccg cagatgcagt tgctgaagaa gctgttcgtg caggacaata cttcatcaac 360
caccgttggt tgggtggaac tcttactaac tggggaacta tccaaaaacg tatcgctcgt 420
ttgaaagaaa tcaaacgtat ggaagaagaa ggaatcttcg acgttcttcc taagaaagaa 480
gttgcacttc ttaataaaca gcgtgcacgt cttgaaaaat tcttgggtgg tatcgaagac 540
atgcctcgta tcccagatgt aatgtacgta gttgacccac ataaagagca aatcgctgtt 600
aaagaagcta aaaaattggg tatcccagtt gtagcgatgg ttgacacaaa cactgatcca 660
gacgatatcg atgtaatcat cccagctaac gatgacgcta tccgcgcggt taaactgatc 720
acagctaaat tggctgacgc tatcatcgaa ggacgtcaag gtgaagatgc aacagcagtt 780
gaagcagaat ttgcagcttc agaagctcaa gcagactcaa tcgaagaaat cgttgaagtt 840
gtagaaggcg acaacgctta a 861
<210> 2
<211> 900
<212> DNA
<213> artificial sequence
<220>
<223> biomarker nucleotide sequences
<400> 2
ttggcaatta acgcacaaga aatcagcgct ttaattaagc aacaaattga aaatttcaaa 60
cccaattttg atgtgactga aacaggtgtt gtaacctata tcggggacgg tatcgcgcgt 120
gctcacggcc ttgaaaatgc catgagtgga gagttgttga tttttgaaaa cggctcttat 180
ggtatggctc aaaacttgga gtcaacagat gttggtatta tcatcctagg tgactttaca 240
gatatccgtg aaggtgatac aattcgccgt acaggtaaaa tcatggaagt cccaataggt 300
gaaagtctga ttggtcgtgt tgtggatcct cttggtcgtc cagttgacgg tcttggagaa 360
atccacactg ataaaactcg tccagttgaa gcgccagctc ctggtgttat gcaacgtaag 420
tctgtatcag aaccattgca aactggtttg aaagctatcg acgcccttgt accgattggt 480
cgtggtcaac gtgagttgat tatcggtgac cgtcagacag ggaaaacaac tattgcgatt 540
gatacaatct tgaaccaaaa aggtcaagat atgatctgta tttatgtcgc tattggacaa 600
aaagaatcaa cagttcgtac gcaagtagaa acactacgtc agtacggtgc cttggactac 660
acaatcgttg tgactgcctc tgcttcacaa ccatctccat tgcttttcct agctccttat 720
gctggggttg ccatggcgga agaatttatg taccaaggca agcatgtttt gatcgtttat 780
gatgatcttt caaaacaagc ggtcgcttat cgtgaacttt ctctcttgct tcgtcgtcca 840
ccaggtcgtg aagccttccc aggggatgtt ttctaccttc acagccgttt gcttgagcgc 900
<210> 3
<211> 402
<212> DNA
<213> artificial sequence
<220>
<223> biomarker nucleotide sequences
<400> 3
aaggcactcg agggagcgct ccgcgacctc accgcgatca ccggccagaa gcccgtgacc 60
acccgcgcca agaagtccat cgcgcagttc aagctgcgtg agggccaggc gatcggtgcg 120
cacgtcacgc ttcgcggcga ccgcatgtgg gagttcctgg atcgcctcct ctcgacggcg 180
cttccccgta tccgtgactt ccgcggactg tcctccaagc agttcgacgg tcacggcaat 240
tacaccttcg gtctcacgga acagtcgatg ttccacgaga tcgatcagga ctcgatcgac 300
cgagtgcgcg gcatggacat cacggttgtc acctcggcca ccaccgacga agagggtcgc 360
gcccttctcc gtcacctcgg cttcccgttc aaggaggact ga 402
Claims (7)
1. A biomarker for atherosclerotic cardiovascular disease, said biomarker being SEQ ID NO: 1-3.
2. The use of a reagent for detecting the abundance of a biomarker for an atherosclerotic cardiovascular disease in the preparation of a kit for predicting whether a sample is a sample of an atherosclerotic cardiovascular disease,
the sample is a fecal sample, and the biomarker is SEQ ID NO: 1-3.
3. The use according to claim 2, comprising predicting whether the sample is a sample of an atherosclerotic cardiovascular disease by:
(1) Determining the relative abundance of the biomarker of claim 2 in the sample; and
(2) Predicting whether the sample is a sample of an atherosclerotic cardiovascular disease based on the relative abundance of the biomarker of claim 2.
4. The use of claim 3, wherein step (2) comprises comparing the relative abundance of the biomarker to a preset threshold.
5. The use of claim 4, wherein the preset threshold is obtained based on a plurality of control samples having a known condition of atherosclerotic cardiovascular disease.
6. The use of claim 5, wherein the preset threshold is at least 0.5.
7. The use of claim 3, wherein the relative abundance of the biomarker is obtained by sequencing.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2017/095350 WO2019023917A1 (en) | 2017-07-31 | 2017-07-31 | Biomarkers for atherosclerotic cardiovascular diseases |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110914453A CN110914453A (en) | 2020-03-24 |
| CN110914453B true CN110914453B (en) | 2023-12-19 |
Family
ID=65232640
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201780093309.1A Active CN110914453B (en) | 2017-07-31 | 2017-07-31 | Biomarkers for atherosclerotic cardiovascular disease |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN110914453B (en) |
| WO (1) | WO2019023917A1 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110277137B (en) * | 2019-06-13 | 2022-03-18 | 南方医科大学顺德医院(佛山市顺德区第一人民医院) | Gene chip information processing system and method for detecting coronary heart disease |
| CN112305119B (en) * | 2020-10-30 | 2021-08-17 | 河北医科大学第二医院 | Biomarker for atherosclerotic cerebral infarction and application thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103160588A (en) * | 2013-04-02 | 2013-06-19 | 山东大学 | Atherosclerosis-related serum miRNA (microribonucleic acid) marker group, and specific primers and application thereof |
| CN105102984A (en) * | 2012-12-28 | 2015-11-25 | 美迪恩斯生命科技株式会社 | Use of sCD14 or its fragments or derivatives for risk stratisfaction, diagnosis and prognosis |
| WO2016049918A1 (en) * | 2014-09-30 | 2016-04-07 | Bgi Shenzhen Co., Limited | Biomarkers for coronary artery disease |
| WO2016049920A1 (en) * | 2014-09-30 | 2016-04-07 | Bgi Shenzhen Co., Limited | Biomarkers for coronary artery disease |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3034317B1 (en) * | 2015-03-31 | 2018-09-14 | International Nutrition Research Company | COMPOSITION FOR THE TREATMENT OF A PATHOGENIC METABOLIC STATE OF THE INTESTINAL MICROBIOTE AND DERIVED DISEASES |
-
2017
- 2017-07-31 WO PCT/CN2017/095350 patent/WO2019023917A1/en active Application Filing
- 2017-07-31 CN CN201780093309.1A patent/CN110914453B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105102984A (en) * | 2012-12-28 | 2015-11-25 | 美迪恩斯生命科技株式会社 | Use of sCD14 or its fragments or derivatives for risk stratisfaction, diagnosis and prognosis |
| CN103160588A (en) * | 2013-04-02 | 2013-06-19 | 山东大学 | Atherosclerosis-related serum miRNA (microribonucleic acid) marker group, and specific primers and application thereof |
| WO2016049918A1 (en) * | 2014-09-30 | 2016-04-07 | Bgi Shenzhen Co., Limited | Biomarkers for coronary artery disease |
| WO2016049920A1 (en) * | 2014-09-30 | 2016-04-07 | Bgi Shenzhen Co., Limited | Biomarkers for coronary artery disease |
Non-Patent Citations (5)
| Title |
|---|
| Holder,M.E.等.ACCESSION No.CP012072.1 Actinomyces meyeri strain W712, complete genome.《GenBank数据库》.2015,FEATURES. * |
| Petrosyan,V.等.ACCESSION No.CP014326.1 Streptococcus mitis strain SVGS_061 chromosome, complete genome.《GenBank数据库》.2016,FEATURES. * |
| Roach,D.J.等.ACCESSION No.NZ_JVPZ01000003.1 Streptococcus oralis strain 206_SPSE 186_140899_6709677_0__..._183_, whole genome shotgun sequence.《GenBank数据库》.2017,FEATURES. * |
| Tomaya Yamashita.Intestinal Immunity and Gut Microbiota in Atherogenesis.《J Atheroscler Thromb》.2017,第24卷(第2期),第110-119页. * |
| Tomoya Yamashita等.Intestinal Immunity and Gut Microbiota as Therapeutic Targets for Preventing Atherosclerotic Cardiovascular Diseases.《Circulation Journal》.2015,第79卷(第9期),第1882-1890页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110914453A (en) | 2020-03-24 |
| WO2019023917A1 (en) | 2019-02-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sood et al. | A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status | |
| CN107075563B (en) | Biomarkers for coronary artery disease | |
| CN107075453B (en) | Biomarkers for coronary artery disease | |
| EP3245298B1 (en) | Biomarkers for colorectal cancer related diseases | |
| CN106399304B (en) | A kind of SNP marker relevant to breast cancer | |
| CN114277143B (en) | Application of exosomes ARPC5, CDA and the like in lung cancer diagnosis | |
| CN113881770B (en) | Reagent kit and device for lung cancer diagnosis | |
| CN110914453B (en) | Biomarkers for atherosclerotic cardiovascular disease | |
| CN108866187B (en) | Long-chain non-coding RNA marker related to lung cancer auxiliary diagnosis and application thereof | |
| WO2015079060A2 (en) | Mirnas as advanced diagnostic tool in patients with cardiovascular disease, in particular acute myocardial infarction (ami) | |
| EP3976810A1 (en) | Methods and systems for urine-based detection of urologic conditions | |
| CN106636351B (en) | One kind SNP marker relevant to breast cancer and its application | |
| CN113801936B (en) | Kit, device and method for lung cancer diagnosis | |
| WO2019051678A1 (en) | Biomarker for atherosclerotic cardiovascular diseases | |
| CN106520957B (en) | The susceptible SNP site detection reagent of DHRS7 and its kit of preparation | |
| CN106868191B (en) | Application of the eukaryotic translation elongation factors in detection breast cancer reagent | |
| CN106811528B (en) | A kind of breast cancer is cured the disease gene new mutation and its application | |
| CN117778566A (en) | Marker for predicting thyroid cancer metastasis and application thereof | |
| WO2016049917A1 (en) | Biomarkers for obesity related diseases |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |