Dataset Information

Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integration.

ABSTRACT:

Objective

To identify OA subtypes based on cartilage transcriptomic data in cartilage tissue and characterize their underlying pathophysiological processes and/or clinically relevant characteristics.

Methods

This study includes n = 66 primary OA patients (41 knees and 25 hips), who underwent a joint replacement surgery, from which macroscopically unaffected (preserved, n = 56) and lesioned (n = 45) OA articular cartilage were collected [Research Arthritis and Articular Cartilage (RAAK) study]. Unsupervised hierarchical clustering analysis on preserved cartilage transcriptome followed by clinical data integration was performed. Protein-protein interaction (PPI) followed by pathway enrichment analysis were done for genes significant differentially expressed between subgroups with interactions in the PPI network.

Results

Analysis of preserved samples (n = 56) resulted in two OA subtypes with n = 41 (cluster A) and n = 15 (cluster B) patients. The transcriptomic profile of cluster B cartilage, relative to cluster A (DE-AB genes) showed among others a pronounced upregulation of multiple genes involved in chemokine pathways. Nevertheless, upon investigating the OA pathophysiology in cluster B patients as reflected by differentially expressed genes between preserved and lesioned OA cartilage (DE-OA-B genes), the chemokine genes were significantly downregulated with OA pathophysiology. Upon integrating radiographic OA data, we showed that the OA phenotype among cluster B patients, relative to cluster A, may be characterized by higher joint space narrowing (JSN) scores and low osteophyte (OP) scores.

Conclusion

Based on whole-transcriptome profiling, we identified two robust OA subtypes characterized by unique OA, pathophysiological processes in cartilage as well as a clinical phenotype. We advocate that further characterization, confirmation and clinical data integration is a prerequisite to allow for development of treatments towards personalized care with concurrently more effective treatment response.

SUBMITTER: Coutinho de Almeida R

PROVIDER: S-EPMC7937023 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integration.

Coutinho de Almeida Rodrigo R Mahfouz Ahmed A Mei Hailiang H Houtman Evelyn E den Hollander Wouter W Soul Jamie J Suchiman Eka E Lakenberg Nico N Meessen Jennifer J Huetink Kasper K Nelissen Rob G H H RGHH Ramos Yolande F M YFM Reinders Marcel M Meulenbelt Ingrid I

Rheumatology (Oxford, England) 20210301 3

<h4>Objective</h4>To identify OA subtypes based on cartilage transcriptomic data in cartilage tissue and characterize their underlying pathophysiological processes and/or clinically relevant characteristics.<h4>Methods</h4>This study includes n = 66 primary OA patients (41 knees and 25 hips), who underwent a joint replacement surgery, from which macroscopically unaffected (preserved, n = 56) and lesioned (n = 45) OA articular cartilage were collected [Research Arthritis and Articular Cartilage ( ...[more]

PMID: 32885253

Similar Datasets

Project description:Background & aimsEsophageal adenocarcinomas (EACs) are heterogeneous and often preceded by Barrett's esophagus (BE). Many genomic changes have been associated with development of BE and EAC, but little is known about epigenetic alterations. We performed epigenetic analyses of BE and EAC tissues and combined these data with transcriptome and genomic data to identify mechanisms that control gene expression and genome integrity.MethodsIn a retrospective cohort study, we collected tissue samples and clinical data from 150 BE and 285 EAC cases from the Oesophageal Cancer Classification and Molecular Stratification consortium in the United Kingdom. We analyzed methylation profiles of all BE and EAC tissues and assigned them to subgroups using non-negative matrix factorization with k-means clustering. Data from whole-genome sequencing and transcriptome studies were then incorporated; we performed integrative methylation and RNA-sequencing analyses to identify genes that were suppressed with increased methylation in promoter regions. Levels of different immune cell types were computed using single-sample gene set enrichment methods. We derived 8 organoids from 8 EAC tissues and tested their sensitivity to different drugs.ResultsBE and EAC samples shared genome-wide methylation features, compared with normal tissues (esophageal, gastric, and duodenum; controls) from the same patients and grouped into 4 subtypes. Subtype 1 was characterized by DNA hypermethylation with a high mutation burden and multiple mutations in genes in cell cycle and receptor tyrosine signaling pathways. Subtype 2 was characterized by a gene expression pattern associated with metabolic processes (ATP synthesis and fatty acid oxidation) and lack methylation at specific binding sites for transcription factors; 83% of samples of this subtype were BE and 17% were EAC. The third subtype did not have changes in methylation pattern, compared with control tissue, but had a gene expression pattern that indicated immune cell infiltration; this tumor type was associated with the shortest time of patient survival. The fourth subtype was characterized by DNA hypomethylation associated with structure rearrangements, copy number alterations, with preferential amplification of CCNE1 (cells with this gene amplification have been reported to be sensitive to CDK2 inhibitors). Organoids with reduced levels of MGMT and CHFR expression were sensitive to temozolomide and taxane drugs.ConclusionsIn a comprehensive integrated analysis of methylation, transcriptome, and genome profiles of more than 400 BE and EAC tissues, along with clinical data, we identified 4 subtypes that were associated with patient outcomes and potential responses to therapy.

Project description:Little is known about the molecular pathogenesis of schizophrenia, possibly because of unrecognized heterogeneity in diagnosed patient populations. We analyzed gene expression data collected from the dorsolateral prefrontal cortex (DLPFC) of post-mortem frozen brains of 189 adult diagnosed schizophrenics and 206 matched controls. Transcripts from 633 genes are differentially expressed in the DLPFC of schizophrenics as compared to controls at Bonferroni-corrected significance levels. Seventeen of those genes are differentially expressed at very high significance levels (<10-8 after Bonferroni correction). The findings were closely replicated in a dataset from an entirely unrelated source. The statistical significance of this differential gene expression is being driven by about half of the schizophrenic DLPFC samples, and importantly, it is the same half of the samples that is driving the significance for almost all of the differentially expressed transcripts. Weighted gene co-expression network analysis (WGCNA) of the schizophrenic subjects, based on the transcripts differentially expressed in the schizophrenics as compared to controls, divides them into two groups. "Type 1" schizophrenics have a DLPFC transcriptome similar to that of controls with only four differentially expressed genes identified. "Type 2" schizophrenics have a DLPFC transcriptome dramatically different from that of controls, with 3529 expression array probes to 3092 genes detecting transcripts that are differentially expressed at very high significance levels. These findings were re-tested and replicated in a separate independent cohort, using the RNAseq data from the DLPFC of an independent set of schizophrenics and control subjects. We suggest the hypothesis that these striking differences in DLPFC transcriptomes, identified and replicated in two populations, imply a fundamental biologic difference between these two groups of diagnosed schizophrenics, and we propose specific paths for further testing and expanding the hypothesis.

Project description:BackgroundOsteoarthritis (OA) is a degenerative disease with a high incidence worldwide. Most affected patients do not exhibit obvious discomfort symptoms or imaging findings until OA progresses, leading to irreversible destruction of articular cartilage and bone. Therefore, developing new diagnostic biomarkers that can reflect articular cartilage injury is crucial for the early diagnosis of OA. This study aims to explore biomarkers related to the immune microenvironment of OA, providing a new research direction for the early diagnosis and identification of risk factors for OA.MethodsWe screened and downloaded relevant data from the Gene Expression Omnibus (GEO) database, and the immune microenvironment-related genes (Imr-DEGs) were identified using the ImmPort data set by combining weighted coexpression analysis (WGCNA). Functional enrichment of GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to explore the correlation of Imr-DEGs. A random forest machine learning model was constructed to analyze the characteristic genes of OA, and the diagnostic significance was determined by the Receiver Operating Characteristic Curve (ROC) curve, with external datasets used to verify the diagnostic ability. Different immune subtypes of OA were identified by unsupervised clustering, and the function of these subtypes was analyzed by gene set enrichment analysis (GSVA). The Drug-Gene Interaction Database was used to explore the relationship between characteristic genes and drugs.ResultsSingle sample gene set enrichment analysis (ssGSEA) revealed that 16 of 28 immune cell subsets in the dataset significantly differed between OA and normal groups. There were 26 Imr-DEGs identified by WGCNA, showing that functional enrichment was related to immune response. Using the random forest machine learning model algorithm, nine characteristic genes were obtained: BLNK (AUC = 0.809), CCL18 (AUC = 0.692), CD74 (AUC = 0.794), CSF1R (AUC = 0.835), RAC2 (AUC = 0.792), INSR (AUC = 0.765), IL11 (AUC = 0.662), IL18 (AUC = 0.699), and TLR7 (AUC = 0.807). A nomogram was constructed to predict the occurrence and development of OA, and the calibration curve confirmed the accuracy of these 9 genes in OA diagnosis.ConclusionThis study identified characteristic genes related to the immune microenvironment in OA, providing new insight into the risk factors of OA.

Dataset Information

Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integration.

Objective

Methods

Results

Conclusion

Publications

Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integration.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets