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Heterogeneity of Synovial Molecular Patterns in Patients with Arthritis

Publication Date: April 30, 2015
Journal: PLOS ONE
Authors: Bernard R. Lauwerys, Daniel Hernández Lobato, Pierre Gramme, Julie Ducreux, et al
Volume: 10
Issue: 4
Pages: e0122104
DOI: https://dx.plos.org/10.1371/journal.pone.0122104
Publisher URL: http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0122104
PubMed: http://www.ncbi.nlm.nih.gov/pubmed/25927832
PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415786
Europe PMC: http://europepmc.org/abstract/MED/25927832
Web of Science: 000353713100007
Scopus: 84928608651
Mendeley: http://www.mendeley.com/research/heterogeneity-synovial-molecular-patterns-patients-arthritis-5

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CiteULike03 May 02:17 UTC

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Mendeley 1029 Apr 17:09 UTC

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{"title"=>"Heterogeneity of synovial molecular patterns in patients with arthritis", "type"=>"journal", "authors"=>[{"first_name"=>"Bernard R.", "last_name"=>"Lauwerys", "scopus_author_id"=>"6603423336"}, {"first_name"=>"Daniel", "last_name"=>"Hernández-Lobato", "scopus_author_id"=>"14919612400"}, {"first_name"=>"Pierre", "last_name"=>"Gramme", "scopus_author_id"=>"56609238300"}, {"first_name"=>"Julie", "last_name"=>"Ducreux", "scopus_author_id"=>"25631760300"}, {"first_name"=>"Adrien", "last_name"=>"Dessy", "scopus_author_id"=>"6505674513"}, {"first_name"=>"Isabelle", "last_name"=>"Focant", "scopus_author_id"=>"56609191900"}, {"first_name"=>"Jérôme", "last_name"=>"Ambroise", "scopus_author_id"=>"35180900700"}, {"first_name"=>"Bertrand", "last_name"=>"Bearzatto", "scopus_author_id"=>"57193972944"}, {"first_name"=>"Adrien Nzeusseu", "last_name"=>"Toukap", "scopus_author_id"=>"8696740400"}, {"first_name"=>"Benoît J.", "last_name"=>"Van Den Eynde", "scopus_author_id"=>"7007091402"}, {"first_name"=>"Dirk", "last_name"=>"Elewaut", "scopus_author_id"=>"55674488400"}, {"first_name"=>"Jean Luc", "last_name"=>"Gala", "scopus_author_id"=>"7006660744"}, {"first_name"=>"Patrick", "last_name"=>"Durez", "scopus_author_id"=>"6701736580"}, {"first_name"=>"Frédéric A.", "last_name"=>"Houssiau", "scopus_author_id"=>"7005187655"}, {"first_name"=>"Thibault", "last_name"=>"Helleputte", "scopus_author_id"=>"34875092000"}, {"first_name"=>"Pierre", "last_name"=>"Dupont", "scopus_author_id"=>"55879970000"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84928608651", "sgr"=>"84928608651", "pui"=>"604050602", "pmid"=>"25927832", "doi"=>"10.1371/journal.pone.0122104"}, "id"=>"51e81159-b204-3d8d-8e65-30526dc5c39c", "abstract"=>"OBJECTIVES: Early diagnosis of rheumatoid arthritis (RA) is an unmet medical need in the field of rheumatology. Previously, we performed high-density transcriptomic studies on synovial biopsies from patients with arthritis, and found that synovial gene expression profiles were significantly different according to the underlying disorder. Here, we wanted to further explore the consistency of the gene expression signals in synovial biopsies of patients with arthritis, using low-density platforms.\\n\\nMETHODS: Low-density assays (cDNA microarray and microfluidics qPCR) were designed, based on the results of the high-density microarray data. Knee synovial biopsies were obtained from patients with RA, spondyloarthropathies (SA) or osteoarthritis (OA) (n = 39), and also from patients with initial undifferentiated arthritis (UA) (n = 49).\\n\\nRESULTS: According to high-density microarray data, several molecular pathways are differentially expressed in patients with RA, SA and OA: T and B cell activation, chromatin remodelling, RAS GTPase activation and extracellular matrix regulation. Strikingly, disease activity (DAS28-CRP) has a significant influence on gene expression patterns in RA samples. Using the low-density assays, samples from patients with OA are easily discriminated from RA and SA samples. However, overlapping molecular patterns are found, in particular between RA and SA biopsies. Therefore, prediction of the clinical diagnosis based on gene expression data results in a diagnostic accuracy of 56.8%, which is increased up to 98.6% by the addition of specific clinical symptoms in the prediction algorithm. Similar observations are made in initial UA samples, in which overlapping molecular patterns also impact the accuracy of the diagnostic algorithm. When clinical symptoms are added, the diagnostic accuracy is strongly improved.\\n\\nCONCLUSIONS: Gene expression signatures are overall different in patients with OA, RA and SA, but overlapping molecular signatures are found in patients with these conditions. Therefore, an accurate diagnosis in patients with UA requires a combination of gene expression and clinical data.", "link"=>"http://www.mendeley.com/research/heterogeneity-synovial-molecular-patterns-patients-arthritis-5", "reader_count"=>10, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>1, "Student > Bachelor"=>1, "Professor"=>2, "Student > Master"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>1, "Student > Bachelor"=>1, "Professor"=>2, "Student > Master"=>1}, "reader_count_by_subject_area"=>{"Medicine and Dentistry"=>6, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Chemistry"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Chemistry"=>{"Chemistry"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Germany"=>1}, "group_count"=>0}

ORCID01 May 03:11 UTC

Scopus | Further Information

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Web of Science®04 Apr 04:31 UTC

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Journal Comments23 Oct 16:05 UTC

{"id"=>"595247559104409600", "text"=>"The study supporting #RheumaKit as a differential diagnosis tool is now published online! @PLOS http://t.co/EIqH7BHzaw", "created_at"=>"2015-05-04T15:24:11Z", "user"=>"RheumaKit", "user_name"=>"RheumaKit", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000673490894/85dc328e07d56b0fa97792f249ef76db_normal.png"}

Wikipedia18 Mar 08:33 UTC

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Figshare 37612 May 04:18 UTC

Figshare

{"files"=>["https://ndownloader.figshare.com/files/2047350"], "description"=>"Both matrices show the nearest neighbors of each biopsy sample from the cohort of patients with a known diagnosis (n = 39). Each sample is represented by a column, and its nearest neighbors are greyed out in that column. The cell on the diagonal is red if the sample is misclassified and black otherwise. Samples from patients with the same diagnosis are surrounded by a dashed square. (A) Nearest neighbors are determined using clinical data only (ρ = 1). More than 5 nearest neighbors are displayed for each sample due to the presence of ties. (B) Nearest neighbors are determined using a combination of clinical and low-density array data (ρ = 0.5), resulting in a correct classification of the last 4 SA samples thanks to good tie-breaking.", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400224, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.g005", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_low_density_microarray_data_on_the_determination_of_the_nearest_neighbors_/1400224", "title"=>"Impact of low-density microarray data on the determination of the nearest neighbors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047351"], "description"=>"Independent sets of samples were hybridized on high-density (HGU133 Plus 2.0 GeneChip) and analyzed by qPCR (Taqman low density array). (A) Differences in mean (log2-transformed) gene expression values between OA and (RA+SA) samples are displayed for the samples analyzed using high-density arrays (x axis) versus qPCR (y axis). (B) The same data from OA and (RA+SA) samples are displayed after normalization of each mean (log2-transformed) gene expression value by its standard deviation.", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400225, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.g006", "stats"=>{"downloads"=>2, "page_views"=>52, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_gene_expression_differences_between_samples_from_patients_with_OA_RA_and_SA_using_high_density_arrays_versus_qPCR_/1400225", "title"=>"Comparison of gene expression differences between samples from patients with OA, RA and SA, using high-density arrays versus qPCR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047352"], "description"=>"Both matrices show the nearest neighbors of each biopsy sample from the cohort of patients with UA, for whom qPCR data are available (n = 31). Each sample is represented by a column, and its nearest neighbors are greyed out in that column. The cell on the diagonal is red if the sample is misclassified and black otherwise. Samples from patients with the same diagnosis are surrounded by a dashed square. (A) Nearest neighbors are determined using only clinical data (ρ = 1). More than 5 nearest neighbors are displayed for each sample due to the presence of ties. (B) Nearest neighbors are determined using a combination of clinical and qPCR data (ρ = 0.2), demonstrating the tie-breaking effect of the qPCR data.", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400226, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.g007", "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_qPCR_data_on_the_determination_of_the_nearest_neighbors_in_UA_samples_/1400226", "title"=>"Impact of qPCR data on the determination of the nearest neighbors in UA samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047353"], "description"=>"Baseline characteristics of the patients with a known diagnosis at the time of the needle-arthroscopy procedure.", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400227, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.t001", "stats"=>{"downloads"=>9, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Baseline_characteristics_of_the_patients_with_a_known_diagnosis_at_the_time_of_the_needle_arthroscopy_procedure_/1400227", "title"=>"Baseline characteristics of the patients with a known diagnosis at the time of the needle-arthroscopy procedure.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047354"], "description"=>"Baseline characteristics of the patients with UA, categorized according to the development of RA after a median follow-up of 9 months (range – months).", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400228, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.t002", "stats"=>{"downloads"=>4, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Baseline_characteristics_of_the_patients_with_UA_categorized_according_to_the_development_of_RA_after_a_median_follow_up_of_9_months_range_8211_months_/1400228", "title"=>"Baseline characteristics of the patients with UA, categorized according to the development of RA after a median follow-up of 9 months (range – months).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047355", "https://ndownloader.figshare.com/files/2047356", "https://ndownloader.figshare.com/files/2047357", "https://ndownloader.figshare.com/files/2047358"], "description"=>"<div>ObjectivesEarly diagnosis of rheumatoid arthritis (RA) is an unmet medical need in the field of rheumatology. Previously, we performed high-density transcriptomic studies on synovial biopsies from patients with arthritis, and found that synovial gene expression profiles were significantly different according to the underlying disorder. Here, we wanted to further explore the consistency of the gene expression signals in synovial biopsies of patients with arthritis, using low-density platforms.MethodsLow-density assays (cDNA microarray and microfluidics qPCR) were designed, based on the results of the high-density microarray data. Knee synovial biopsies were obtained from patients with RA, spondyloarthropathies (SA) or osteoarthritis (OA) (n = 39), and also from patients with initial undifferentiated arthritis (UA) (n = 49).ResultsAccording to high-density microarray data, several molecular pathways are differentially expressed in patients with RA, SA and OA: T and B cell activation, chromatin remodelling, RAS GTPase activation and extracellular matrix regulation. Strikingly, disease activity (DAS28-CRP) has a significant influence on gene expression patterns in RA samples. Using the low-density assays, samples from patients with OA are easily discriminated from RA and SA samples. However, overlapping molecular patterns are found, in particular between RA and SA biopsies. Therefore, prediction of the clinical diagnosis based on gene expression data results in a diagnostic accuracy of 56.8%, which is increased up to 98.6% by the addition of specific clinical symptoms in the prediction algorithm. Similar observations are made in initial UA samples, in which overlapping molecular patterns also impact the accuracy of the diagnostic algorithm. When clinical symptoms are added, the diagnostic accuracy is strongly improved.ConclusionsGene expression signatures are overall different in patients with OA, RA and SA, but overlapping molecular signatures are found in patients with these conditions. Therefore, an accurate diagnosis in patients with UA requires a combination of gene expression and clinical data.</div>", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400229, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0122104.s001", "https://dx.doi.org/10.1371/journal.pone.0122104.s002", "https://dx.doi.org/10.1371/journal.pone.0122104.s003", "https://dx.doi.org/10.1371/journal.pone.0122104.s004"], "stats"=>{"downloads"=>12, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heterogeneity_of_Synovial_Molecular_Patterns_in_Patients_with_Arthritis_/1400229", "title"=>"Heterogeneity of Synovial Molecular Patterns in Patients with Arthritis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047346"], "description"=>"Lists of genes of progressively decreasing sizes were determined based on high-density transcriptomic data, and used in order to predict diagnosis in 25 patients with RA, SLE, OA, SA and MIC. BCR is plotted in function of the signature size. Lists of genes containing between 20 and 100 probe sets provide performances that range between 83% and 85%.", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400220, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.g001", "stats"=>{"downloads"=>5, "page_views"=>147, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Balanced_classification_rate_BCR_of_a_nearest_neighbor_classifier_as_a_function_of_the_signature_size_number_of_genes_used_for_prediction_/1400220", "title"=>"Balanced classification rate (BCR) of a nearest neighbor classifier as a function of the signature size (number of genes) used for prediction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047347"], "description"=>"Analyses performed on high density transcriptomic data resulted in the selection of 100 probe sets differentiating patients with RA, SLE, OA, MIC and SA. The probes and gene symbols are also listed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122104#pone.0122104.s004\" target=\"_blank\">S3 Table</a>. (A) Hierarchical clustering algorithms using the high density gene expression values of these genes (and based on the Pearson correlation distance) distribute the samples into “inflammatory” (RA and SLE) and “high extra-cellular matrix turn-over” (OA, SA and MIC) clusters. They also identify diagnostic subdivisions. (B) The high density gene expression values of these 100 genes are displayed according to the clinical diagnosis of the samples.", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400221, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.g002", "stats"=>{"downloads"=>3, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_High_density_gene_expression_data_used_for_the_design_of_the_low_density_platform_/1400221", "title"=>"High-density gene expression data used for the design of the low-density platform.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047348"], "description"=>"Mean centered log2-transformed expression levels of selected T cell activation-associated transcripts were extracted from HGU133 Plus2.0 GeneChip array data sets of 32 patients with RA. DAS28-CRP scores were retrieved from the medical files of the patients, and the samples are sorted by ascending DAS28-CRP. Correlation coefficients (Pearson r) between gene expression and DAS28-CRP are displayed for each transcript.", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400222, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.g003", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_disease_activity_on_gene_expression_in_RA_samples_/1400222", "title"=>"Effect of disease activity on gene expression in RA samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-30 04:25:00"}
{"files"=>["https://ndownloader.figshare.com/files/2047349"], "description"=>"Independent sets of samples were hybridized on high-density (HGU133 Plus 2.0 GeneChip) and low-density (DualChip) microarrays. (A) Differences in mean (log2-transformed) gene expression values between OA and (RA+SA) samples are displayed for the samples hybridized on high-density (x axis) versus low-density (y axis) arrays. (B) The same data from OA and (RA+SA) samples are displayed after normalization of each mean (log2-transformed) gene expression value by its standard deviation. (C) Normalized TCR gamma alternate reading frame protein (TARP), lymphocyte-specific protein tyrosine kinase (LCK) and Interleukin-7 Receptor (IL7R) gene expression data in OA versus RA and SA samples observed using low-density arrays. (D) Normalized Placental Growth Factor (PGF) gene expression data in OA versus RA and SA samples observed using low-density arrays. Mean values are represented by a horizontal bar. p values are calculated using Student’s t tests.", "links"=>[], "tags"=>["microarray data", "biopsy", "oa", "gene expression signals", "B cell activation", "improved.ConclusionsGene expression signatures", "RAS GTPase activation", "gene expression patterns", "ua", "extracellular matrix regulation", "Knee synovial biopsies", "gene expression data results", "sa", "sample", "Synovial Molecular Patterns", "das", "synovial gene expression profiles", "Arthritis ObjectivesEarly diagnosis", "synovial biopsies", "arthritis"], "article_id"=>1400223, "categories"=>["Biological Sciences"], "users"=>["Bernard R. Lauwerys", "Daniel Hernández-Lobato", "Pierre Gramme", "Julie Ducreux", "Adrien Dessy", "Isabelle Focant", "Jerôme Ambroise", "Bertrand Bearzatto", "Adrien Nzeusseu Toukap", "Benoit J. Van den Eynde", "Dirk Elewaut", "Jean-Luc Gala", "Patrick Durez", "Frédéric A. Houssiau", "Thibault Helleputte", "Pierre Dupont"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122104.g004", "stats"=>{"downloads"=>0, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_gene_expression_differences_between_samples_from_patients_with_OA_RA_and_SA_using_high_density_versus_low_density_microarrays_/1400223", "title"=>"Comparison of gene expression differences between samples from patients with OA, RA and SA, using high-density versus low-density microarrays.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-30 04:25:00"}

PMC Usage Stats 101917 Mar 19:39 UTC

PMC Usage Stats | Further Information

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Relative Metric10 May 17:55 UTC

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}

F1000Prime07 Apr 12:07 UTC

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