WO2016049936A1 - Biomarkers for rheumatoid arthritis and usage therof - Google Patents

Biomarkers for rheumatoid arthritis and usage therof Download PDF

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WO2016049936A1
WO2016049936A1 PCT/CN2014/088068 CN2014088068W WO2016049936A1 WO 2016049936 A1 WO2016049936 A1 WO 2016049936A1 CN 2014088068 W CN2014088068 W CN 2014088068W WO 2016049936 A1 WO2016049936 A1 WO 2016049936A1
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biomarker
rheumatoid arthritis
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relative abundance
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PCT/CN2014/088068
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English (en)
French (fr)
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Qiang FENG
Dongya ZHANG
Huijue JIA
Donghui Wang
Jun Wang
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Bgi Shenzhen Co., Limited
Bgi Shenzhen
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Priority to PCT/CN2014/088068 priority Critical patent/WO2016049936A1/en
Priority to CN201480082373.6A priority patent/CN107002021B/zh
Priority to PCT/CN2015/083490 priority patent/WO2016050111A1/en
Priority to US15/515,367 priority patent/US10883146B2/en
Priority to AU2015327511A priority patent/AU2015327511B2/en
Priority to KR1020177011630A priority patent/KR101986442B1/ko
Priority to CN201580053212.9A priority patent/CN108064272B/zh
Priority to CA2963013A priority patent/CA2963013C/en
Priority to PCT/CN2015/083488 priority patent/WO2016050110A1/en
Priority to DK15847187.0T priority patent/DK3201317T3/da
Priority to CN201580053213.3A priority patent/CN108064263B/zh
Priority to EP15847187.0A priority patent/EP3201317B1/en
Priority to RU2017115001A priority patent/RU2691375C2/ru
Priority to JP2017518141A priority patent/JP6485843B2/ja
Publication of WO2016049936A1 publication Critical patent/WO2016049936A1/en
Priority to HK18108502.4A priority patent/HK1248753A1/zh
Priority to HK18108512.2A priority patent/HK1248764A1/zh

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N1/00—Microorganisms, e.g. protozoa; Compositions thereof; Processes of propagating, maintaining or preserving microorganisms or compositions thereof; Processes of preparing or isolating a composition containing a microorganism; Culture media therefor
    • C12N1/20—Bacteria; Culture media therefor
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/10—Musculoskeletal or connective tissue disorders
    • G01N2800/101—Diffuse connective tissue disease, e.g. Sjögren, Wegener's granulomatosis
    • G01N2800/102—Arthritis; Rheumatoid arthritis, i.e. inflammation of peripheral joints

Definitions

  • the present invention relates biomedical filed, and particularly to biomarkers and methods for predicting the risk of a disease related to microbiota, in particular rheumatoid arthritis (RA) .
  • RA rheumatoid arthritis
  • RA Rheumatoid arthritis
  • DMARD disease-modifying antirheumatic drugs
  • the gut microbiota is a key environmental factor for human health, with established roles in obesity, diabetes, colon cancer, etc. . Besides functioning in nutrient and xenobiotic metabolism, microbes in the distal gut crosstalk with the neuro-immune-endocrine system and the blood stream to impact the entire human body. The gut microbiota is stably associated with a given individual, adding to its value in disease-related investigations. The heterogeneity of the gut microbiome in the human population suggests that treatment of diseases should be personalized according to the gut microbiome, whose role in drug activation or inactivation, immune modulation, etc. remains largely unclear.
  • the oral microbiota is relatively understudied compared to the gut microbiota, with the Human Microbiome Project (HMP) only sampling ⁇ 100 healthy individuals for WGS (Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–21 (2012) , incorporated herein by reference) . Metagenomic analysis of the role of the oral microbiome in diseases has been lacking, despite the fact that dental and salivary samples are more readily available at clinical visits than fecal samples. It is also not known to what extent oral and gut microbial disease markers might converge in their identity or function.
  • HMP Human Microbiome Project
  • Embodiments of the present disclosure seek to solve at least one of the problems existing in the prior art to at least some extent.
  • the present invention is based on the following findings by the inventors:
  • RA rheumatoid arthritis
  • MWAS Metagenome-Wide Association Study
  • the inventors identified and validated gut ⁇ dental ⁇ salivary markers set (29 gut MLGs ⁇ 28 dental MLGs ⁇ 19 salivary MLGs) by a random forest model based on RA-associated genes markers. For intuitive evaluation of the risk of RA disease based on these 29 gut MLGs ⁇ 28 dental MLGs ⁇ 19 salivary MLGs, the inventors calculated probability of illness through a random forest model based on the relative abundance profiles of MLG markers in the training sets respectively.
  • the inventors'data provide insight into the characteristics of the gut ⁇ dental ⁇ salivary metagenome related to RA risk, a paradigm for future studies of the pathophysiological role of the gut ⁇ dental ⁇ salivary metagenome in other relevant disorders, and the potential usefulness for a microbiota-based approach for assessment of individuals at risk of such disorders.
  • the markers of the present invention are specific and sensitive.
  • analysis of stool promises accuracy, safety, affordability, and patient compliance. And samples of stool are transportable.
  • Polymerase chain reaction (PCR) -based assays are comfortable and noninvasive, so people will participate in a given screening program more easily.
  • the markers of the present invention may also serve as tools for therapy monitoring in RA patients to detect the response to therapy.
  • biomarker set for predicting a disease related to microbiota in a subject is provided, and according to embodiments of present disclosure the biomarker set consists of:
  • gut biomarker comprising Bifidobacterium dentium, RA-2633, Enterococcus sp. , RA-781, Gordonibacter pamelaeae, RA-3396, RA-6638, RA-2441, RA-527, Clostridium sp. , RA-2637, Citrobacter sp. , Eubacterium sp. , Citrobacter sp. , RA-3215, Con-1722, Con-4360, Con-4212, Con-1261, Bifidobacterium bifidum, Klebsiella pneumoniae, Con-1423, Veillonella sp. , Con-4095, Con-4103, Con-1735, Con-1710, Con-1832, Con-1170,
  • dental biomarker comprising RA-10848, RA-9842, RA-9941, RA-9938, RA-10684, RA-9998, Con-7913, Con-20702, Con-11, Con-8169, Con-1708, Con-7847, Con-5233, Con-791, Con-5566, Con-4455, Con-13169, Con-6088, Con-5554, Con-14781, Con-2466, Con-483, Con-2562, Con-4701, Con-4824, Con-5030, Con-757, Con-530 , and
  • salivary biomarker comprising RA-27683, RA-9651, RA-13621, RA-27616, Con-6908, Con-305, Con-1559, Con-1374, Con-6746, Campylobacter rectus, Con-1141, Con-20, Streptococcus sp. , Con-1238, Con-1073, Con-636, Con-1, Porphyromonas gingivalis, Lactococcus sp. . ,
  • microbes with genomic DNA comprising at least a partial sequence of SEQ ID NO: 1 to 9319.
  • the biomarker set consists of at least one of the species listed in Table 2-2, preferably at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%of the species listed in Table 2-2.
  • the gut biomarker comprises at least a partial sequence of SEQ ID NO: 1 to 9319 as stated in Table 5.
  • the gut biomarker comprises Bifidobacterium dentium JCVIHMP022, Prevotella copri CB7, DSM 18205, Enterococcus faecium E980, Ruminococcus obeum A2-162, Gordonibacter pamelaeae 7-10-1-bT, DSM 19378, Ruminococcus bromii L2-63, Eubacterium ventriosum ATCC 27560, Klebsiella oxytoca KCTC 1686, Clostridium asparagiforme DSM 15981, Prevotella copri CB7, DSM 18205, Citrobacter freundii 4_7_47CFAA, Eubacterium sp.
  • the dental biomarker comprises Actinomyces sp. oral taxon 180 F0310, Rothia mucilaginosa DY-18, Actinomyces graevenitzii C83, Actinomyces odontolyticus ATCC 17982, Veillonella atypica ACS-134-V-Col7a, Actinomyces sp. F0384, Actinomyces sp. oral taxon 848 F0332, Neisseria mucosa M26, ATCC 25996, Actinomyces sp. oral taxon 448 F0400, Tannerella forsythensis ATCC 43037, Actinomyces sp.
  • the salivary biomarker comprising Gemella haemolysans ATCC 10379, Veillonella atypica ACS-049-V-Sch6, Actinomyces odontolyticus ATCC 17982, Actinomyces odontolyticus ATCC 17982, Treponema denticola ATCC 35405, Actinomyces sp. oral taxon 448 F0400, Treponema vincentii ATCC 35580, Streptococcus australis ATCC 700641, Campylobacter rectus RM3267, CCUG 20446, Actinomyces sp.
  • oral taxon 171 F0337 Treponema denticola ATCC 35405, Streptococcus sanguinis VMC66, Actinomyces sp. oral taxon 448 F0400, Actinomyces sp. oral taxon 448 F0400, Neisseria bacilliformis ATCC BAA-1200, Burkholderia mallei PRL-20, Porphyromonas gingivalis TDC60, Lactococcus lactis lactis KF147.
  • a biomarker set for predicting a disease related to microbiota in a subject is provided, according to embodiments of present disclosure, the biomarker set consists of:
  • the gut biomarker comprises at least a partial sequence of SEQ ID NO: 1 to 9319.
  • the disease is rheumatoid arthritis or related disorder.
  • kits for determining the gene marker set described above comprising primers used for PCR amplification and designed according to the DNA sequecne as listed below:
  • the gut biomarker comprises at least a partial sequence of SEQ ID NO: 1 to 9319.
  • kit for determining the gene marker set described above comprising one or more probes designed according to the genes as listed below:
  • the gut biomarker comprises at least a partial sequence of SEQ ID NO: 1 to 9319.
  • the probability of rheumatoid arthritis greater than a cutoff indicates that the subject to be tested has or is at the risk of developing the rheumatoid arthritis or related disorder.
  • the training dataset is constructed based on the relative abundance information of each biomarker of a plurality of subjects having rheumatoid arthritis and a plurality of normal subjects using the Multivariate statistical model, alternatively the Multivariate statistical model is randomForest model.
  • the training dataset is a matrix with each row representing each biomarker of the biomarker set according to any one of claims 1 to 5, each column representing samples, each cell representing relative abundance profile of a biomarker in the sample, and sample disease status is a vectot, with 1 for rheumatoid arthritis and 0 for control.
  • Bifidobacterium dentium JCVIHMP022 Prevotella copri CB7, DSM 18205, Enterococcus faecium E980, Ruminococcus obeum A2-162, Gordonibacter pamelaeae 7-10-1-bT, DSM 19378, Ruminococcus bromii L2-63, Eubacterium ventriosum ATCC 27560, Klebsiella oxytoca KCTC 1686, Clostridium asparagiforme DSM 15981, Prevotella copri CB7, DSM 18205, Citrobacter freundii 4_7_47CFAA, Eubacterium sp.
  • oral taxon 158 F0412, Comamonas testosteroni KF-1, Klebsiella pneumoniae NTUH-K2044, Veillonella atypica ACS-134-V-Col7a, Streptococcus australis ATCC 700641, Parabacteroides merdae ATCC 43184 is obtained based on the relative abundance information of SEQ ID NO: 1 to 9319.
  • the training dataset is at least one of Table 8-1 and Table8-2, and the probability of rheumatoid arthritis being at least 0.5 indicates that the subject to be tested has or is at the risk of developing the rheumatoid arthritis or related disorder.
  • a kit for predicting the risk of rheumatoid arthritis or related disorder in a subject to be tested comprising:
  • the probability of rheumatoid arthritis greater than a cutoff indicates that the subject to be tested has or is at the risk of developing the rheumatoid arthritis or related disorder.
  • the training dataset is constructed based on the relative abundance information of each biomarker of a plurality of subjects having rheumatoid arthritis and a plurality of normal subjects using the Multivariate statistical model, alternatively the Multivariate statistical model is randomForest model.
  • the training dataset is a matrix with each row representing each biomarker of the biomarker set according to any one of claims 1 to 5, each column representing samples, each cell representing relative abundance profile of a biomarker in the sample, and sample disease status is a vectot, with 1 for rheumatoid arthritis and 0 for control.
  • oral taxon 158 F0412, Comamonas testosteroni KF-1, Klebsiella pneumoniae NTUH-K2044, Veillonella atypica ACS-134-V-Col7a, Streptococcus australis ATCC 700641, Parabacteroides merdae ATCC 43184 is obtained based on the relative abundance information of SEQ ID NO: 1 to 9319.
  • the training dataset is at least one of Table 8-1 and Table 8-2, and the probability of rheumatoid arthritis being at least 0.5 indicates that the subject to be tested has or is at the risk of developing the rheumatoid arthritis or related disorder.
  • a method of diagnosing whether a subject has an abnormal condition related to microbiota or is at the risk of developing an abnormal condition related to microbiota comprising:
  • the method comprises:
  • the probability of rheumatoid arthritis greater than a cutoff indicates that the subject to be tested has or is at the risk of developing the rheumatoid arthritis or related disorder.
  • the training dataset is constructed based on the relative abundance information of each biomarker of a plurality of subjects having rheumatoid arthritis and a plurality of normal subjects using the Multivariate statistical model, alternatively the Multivariate statistical model is randomForest model.
  • the training dataset is a matrix with each row representing each biomarker of the biomarker set according to any one of claims 1 to 5, each column representing samples, each cell representing relative abundance profile of a biomarker in the sample, and sample disease status is a vectot, with 1 for rheumatoid arthritis and 0 for control.
  • the training dataset is at least one of Table 8-1 and Table 8-2, and the probability of rheumatoid arthritis being at least 0.5 indicates that the subject to be tested has or is at the risk of developing the rheumatoid arthritis or related disorder.
  • Fig. 1 Gut or oral MLGs allow classification of RA patients from healthy controls.
  • (a, d, f) ROC curve for the fecal
  • the dot marks the false positive and true positive rates for the best cut-off probability.
  • Classification of the fecal test set consisted of 17 controls and 17 RA cases, who are consanguine or non-consanguine of each other.
  • Example 1 Identifying and validating biomarkers for evaluating rheumatoid arthritis risk
  • Lysis was terminated by incubation at 95 °C for 10 min, and the sampled were frozen at -80 °C until transport. DNA extraction was performed following the protocol for fecal samples. For saliva, 100 ⁇ l of saliva was added into 100 ⁇ l of 2x lysis buffer. The posterior pharynx wall was swabbed and added to the same tube. The samples were then lysed and extracted as the dental samples.
  • RA was diagnosed at Peking Union Medical College Hospital according to the 2010 ACR/EULAR classification criteria. All phenotypic information was collected upon the subjects’ initial visit to the hospital following standard procedures. RA patients between 18 and 65 years old, with a disease duration of at least 6 weeks, at least 1 swollen joint and 3 tender joints were enlisted. Patients were excluded if they had a history of chronic serious infection, any current infection or any type of cancer. Pregnant or lactating women were excluded. All patients were informed of the risk of infertility and patients with a desire to have children were excluded. Even though some of the patients had suffered from RA for years, they were DMARD- because they had not been diagnosed with RA at local hospitals before visiting Peking Union Medical College Hospital, and had only taken painkillers to relieve RA symptoms.
  • Paired-end metagenomic sequencing was performed on the Illumina platform (insert size 350bp, read length 100bp) , and the sequencing reads were quality-controlled and de novo assembled into contigs using SOAPdenovo v2.04 (Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012) . , incorporated herein by reference) , as described previously (Qin et al. 2012, supra) .
  • the average rate of host contamination was 0.37%for fecal, 5.55%for dental and 40.85%for saliva samples.
  • Gene prediction from the assembled contigs was performed using GeneMark v2.7d. Redundant genes were removed using BLAT (Kent, W. J. BLAT--the BLAST-like alignment tool. Genome Res. 12, 656–64 (2002) , incorporated herein by reference) with the cutoff of 90%overlap and 95%identity (no gaps allowed) , resulting in a non-redundant gene catalogue of 3, 800, 011 genes for 212 fecal samples (containing 21 of the DMARD-treated samples) , and a catalogue of 3,234, 997 genes for the 203 oral samples (105 dental plaques samples and 98 saliva samples) .
  • the gene catalogue from fecal samples was further integrated into an existing gut microbial reference catalogue of 4.3 million genes using BLAT (95%identity, 90%overlap) (Qin et al. 2012, supra) , resulting in a final catalogue of 5.9 million genes. Relative abundances of the genes were determined by aligning high-quality sequencing reads to the gut or oral reference gene catalogue using the same procedure as in the published T2D paper (Qin et al. 2012, supra) .
  • Taxonomic assignment of the predicted genes was performed according to the IMG database (v400) using an in-house pipeline detailed previously (Qin et al. 2012, supra) , with 70%overlap and 65%identity for assignment to phylum, 85%identity to genus, and 95%identity to species. The relative abundance of a taxon was calculated from the relative abundance of its genes.
  • Taxonomic assignment and abundance profiling of the MLGs were performed according to the taxonomy and the relative abundance of their constituent genes, as previously described (Qin et al.2012, supra) . Briefly, assignment to species requires more than 90%of genes in an MLG to align with the species’ genome with more than 95%identity, 70%overlap of query. Assigning an MLG to a genus requires more than 80%of its genes to align with a genome with 85%identity in both DNA and protein sequences. Average identity with the genome (s) calculated from all genes was shown for reference only. MLGs were further clustered according to Kendall’s correlation between their abundances in all samples regardless of case-control status, and the co-occurrence network was visualized by Cytoscape 3.0.2.
  • a random forest model (R. 2.14, randomForest4.6-7 package) (Liaw, Andy &Wiener, Matthew. Classification and Regression by randomForest, R News (2002) , Vol. 2/3 p. 18, incorporated herein by reference) was trained using the MLG abundance profile of the training cohort (Table 1-2) to select the optimal set of MLG markers. The model was tested on one or more testing sets and the prediction error was calculated.
  • RandomForest4.6-7 package package in R vision 2.14
  • input is a training dataset (namely relative abundance profiles of selected MLGs in training samples)
  • sample disease status sample disease status of training samples is a vectot, 1 for RA, 0 for control
  • testset just the relative abundance profiles of selected MLGs in test set
  • the inventors used the randomForest function from randomForest package in R software to build the classification, and predict function was used to predict the test set.
  • Output is the prediction results (probability of illness; cutoff is 0.5 and if the probability of illness ⁇ 0.5, the subject is at risk of RA)
  • Table 1-2 Sample information of training sets (chosen from samples used for gene catalogue construction in Table 1-1)
  • the inventors first constructed random forest disease classifiers based on the gut MLGs.
  • ROC receiver operating characteristic
  • AUC area under receiver operating characteristic
  • the inventors have identified and validated markers set (29 gut MLGs ⁇ 28 dental MLGs ⁇ 19 salivary MLGs) by a random forest model based on RA-associated genes markers. And the inventors have constructed a RA classifier to evaluate the risk of RA disease based on these RA-associated gut microbiome.

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PCT/CN2014/088068 2014-09-30 2014-09-30 Biomarkers for rheumatoid arthritis and usage therof WO2016049936A1 (en)

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Application Number Priority Date Filing Date Title
PCT/CN2014/088068 WO2016049936A1 (en) 2014-09-30 2014-09-30 Biomarkers for rheumatoid arthritis and usage therof
CN201480082373.6A CN107002021B (zh) 2014-09-30 2014-09-30 类风湿性关节炎的生物标记物及其用途
CA2963013A CA2963013C (en) 2014-09-30 2015-07-07 Biomarkers for rheumatoid arthritis and usage thereof
DK15847187.0T DK3201317T3 (da) 2014-09-30 2015-07-07 Biomarkører for rheumatoid arthritis og anvendelse deraf
AU2015327511A AU2015327511B2 (en) 2014-09-30 2015-07-07 Biomarkers for rheumatoid arthritis and usage thereof
KR1020177011630A KR101986442B1 (ko) 2014-09-30 2015-07-07 류머티스성 관절염용 바이오마커 및 이의 용도
CN201580053212.9A CN108064272B (zh) 2014-09-30 2015-07-07 用于类风湿性关节炎的生物标记物及其用途
PCT/CN2015/083490 WO2016050111A1 (en) 2014-09-30 2015-07-07 Biomarkers for rheumatoid arthritis and usage thereof
PCT/CN2015/083488 WO2016050110A1 (en) 2014-09-30 2015-07-07 Biomarkers for rheumatoid arthritis and usage thereof
US15/515,367 US10883146B2 (en) 2014-09-30 2015-07-07 Biomarkers for rheumatoid arthritis and usage thereof
CN201580053213.3A CN108064263B (zh) 2014-09-30 2015-07-07 用于类风湿性关节炎的生物标记物及其用途
EP15847187.0A EP3201317B1 (en) 2014-09-30 2015-07-07 Biomarkers for rheumatoid arthritis and usage thereof
RU2017115001A RU2691375C2 (ru) 2014-09-30 2015-07-07 Биомаркеры для ревматоидного артрита и их применение
JP2017518141A JP6485843B2 (ja) 2014-09-30 2015-07-07 関節リウマチのバイオマーカー及びその使用
HK18108502.4A HK1248753A1 (zh) 2014-09-30 2018-07-03 用於類風濕性關節炎的生物標記物及其用途
HK18108512.2A HK1248764A1 (zh) 2014-09-30 2018-07-03 用於類風濕性關節炎的生物標記物及其用途

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