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Keywords {🔍}

gene, genes, dlbcl, pubmed, clustering, article, google, scholar, expression, survival, data, cas, subtypes, patients, study, identified, subsets, samples, functional, subtype, lymphoma, analysis, cluster, dataset, based, molecular, bcell, clusters, figure, diffuse, subset, large, twoway, clinical, jaw, algorithm, stable, central, proposed, partitions, set, profiling, sample, disease, cancer, concept, authors, hidden, heterogeneity, bioinformatics,

Topics {✒️}

b-cell cll/lymphoma 7a diffuse large-b-cell lymphomas diffuse large-b-cell lymphoma kaplan-meier product-limit method genome-wide expression patterns open access article b-cell cll/lymphoma 6 lymphoid-restricted membrane protein article download pdf bio-pharmaceutical key laboratory protein-protein interaction studies germinal centre b-cell kaplan-meier survival analysis cell-cell signalling wald chi-square test b-cell lymphomas multi-relational data mining gene expression-based method full size image initially caries-free children log-rank statistic comparing super-paramagnetic clustering technique disease-relevant functional modules germinal centre b-cells gene expression profiling gene-expression profiling diffuse aggressive lymphoma mixture model-based approach b-cell lines [35] b-cell compartments large-scale analysis k-nearest neighbours method modern clinical practice machine-learning-based classification high-correlation sample subsets privacy choices/manage cookies 7-mrna signature enriched germinal centre b related subjects refined clinical practice /cgi-bin/cancer/datasets dna microarray profiling full access original profiling study signal transduction pathway gov/lymphoma/index gene expression profiles profile gene expression gene expression monitoring identify functionally sounding

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WebPage:
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         headline:Unravelling the hidden heterogeneities of diffuse large B-cell lymphoma based on coupled two-way clustering
         description:It becomes increasingly clear that our current taxonomy of clinical phenotypes is mixed with molecular heterogeneity. Of vital importance for refined clinical practice and improved intervention strategies is to define the hidden molecular distinct diseases using modern large-scale genomic approaches. Microarray omics technology has provided a powerful way to dissect hidden genetic heterogeneity of complex diseases. The aim of this study was thus to develop a bioinformatics approach to seek the transcriptional features leading to the hidden subtyping of a complex clinical phenotype. The basic strategy of the proposed method was to iteratively partition in two ways sample and feature space with super-paramagnetic clustering technique and to seek for hard and robust gene clusters that lead to a natural partition of disease samples and that have the highest functionally conceptual consensus evaluated with Gene Ontology. We applied the proposed method to two publicly available microarray datasets of diffuse large B-cell lymphoma (DLBCL), a notoriously heterogeneous phenotype. A feature subset of 30 genes (38 probes) derived from analysis of the first dataset consisting of 4026 genes and 42 DLBCL samples identified three categories of patients with very different five-year overall survival rates (70.59%, 44.44% and 14.29% respectively; p = 0.0017). Analysis of the second dataset consisting of 7129 genes and 58 DLBCL samples revealed a feature subset of 13 genes (16 probes) that not only replicated the findings of the important DLBCL genes (e.g. JAW1 and BCL7A), but also identified three clinically similar subtypes (with 5-year overall survival rates of 63.13%, 34.92% and 15.38% respectively; p = 0.0009) to those identified in the first dataset. Finally, we built a multivariate Cox proportional-hazards prediction model for each feature subset and defined JAW1 as one of the most significant predictor (p = 0.005 and 0.014; hazard ratios = 0.02 and 0.03, respectively for two datasets) for both DLBCL cohorts under study. Our results showed that the proposed algorithm is a promising computational strategy for peeling off the hidden genetic heterogeneity based on transcriptionally profiling disease samples, which may lead to an improved diagnosis and treatment of cancers.
         datePublished:2007-09-22T00:00:00Z
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      headline:Unravelling the hidden heterogeneities of diffuse large B-cell lymphoma based on coupled two-way clustering
      description:It becomes increasingly clear that our current taxonomy of clinical phenotypes is mixed with molecular heterogeneity. Of vital importance for refined clinical practice and improved intervention strategies is to define the hidden molecular distinct diseases using modern large-scale genomic approaches. Microarray omics technology has provided a powerful way to dissect hidden genetic heterogeneity of complex diseases. The aim of this study was thus to develop a bioinformatics approach to seek the transcriptional features leading to the hidden subtyping of a complex clinical phenotype. The basic strategy of the proposed method was to iteratively partition in two ways sample and feature space with super-paramagnetic clustering technique and to seek for hard and robust gene clusters that lead to a natural partition of disease samples and that have the highest functionally conceptual consensus evaluated with Gene Ontology. We applied the proposed method to two publicly available microarray datasets of diffuse large B-cell lymphoma (DLBCL), a notoriously heterogeneous phenotype. A feature subset of 30 genes (38 probes) derived from analysis of the first dataset consisting of 4026 genes and 42 DLBCL samples identified three categories of patients with very different five-year overall survival rates (70.59%, 44.44% and 14.29% respectively; p = 0.0017). Analysis of the second dataset consisting of 7129 genes and 58 DLBCL samples revealed a feature subset of 13 genes (16 probes) that not only replicated the findings of the important DLBCL genes (e.g. JAW1 and BCL7A), but also identified three clinically similar subtypes (with 5-year overall survival rates of 63.13%, 34.92% and 15.38% respectively; p = 0.0009) to those identified in the first dataset. Finally, we built a multivariate Cox proportional-hazards prediction model for each feature subset and defined JAW1 as one of the most significant predictor (p = 0.005 and 0.014; hazard ratios = 0.02 and 0.03, respectively for two datasets) for both DLBCL cohorts under study. Our results showed that the proposed algorithm is a promising computational strategy for peeling off the hidden genetic heterogeneity based on transcriptionally profiling disease samples, which may lead to an improved diagnosis and treatment of cancers.
      datePublished:2007-09-22T00:00:00Z
      dateModified:2007-09-22T00:00:00Z
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      keywords:
         Gene Subset
         DLBCL Patient
         DLBCL Case
         Sample Partition
         DLBCL Subtype
         Life Sciences
         general
         Microarrays
         Proteomics
         Animal Genetics and Genomics
         Microbial Genetics and Genomics
         Plant Genetics and Genomics
      image:
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      name:Shaoqi Rao
      affiliation:
            name:Harbin Medical University
            address:
               name:The First Clinical College, Department of Bioinformatics, and the Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University, Harbin, China
               type:PostalAddress
            type:Organization
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      name:The First Clinical College, Department of Bioinformatics, and the Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University, Harbin, China
      name:Institute of Medical Genetics, Tongji University, Shanghai, China
      name:The First Clinical College, Department of Bioinformatics, and the Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University, Harbin, China
      name:Institute of Medical Genetics, Tongji University, Shanghai, China
      name:Department of Computer Science, Harbin Institute of Technology, Harbin, China
      name:The Biomedical Engineering Institute, Capital Medical University, Beijing, China
      name:The First Clinical College, Department of Bioinformatics, and the Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University, Harbin, China
      name:The First Clinical College, Department of Bioinformatics, and the Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University, Harbin, China
      name:Department of Computer Science, Harbin Institute of Technology, Harbin, China
      name:The Biomedical Engineering Institute, Capital Medical University, Beijing, China
      name:Department of Molecular Cardiology, Cleveland Clinic, Cleveland, USA
      name:The First Clinical College, Department of Bioinformatics, and the Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University, Harbin, China
      name:The Biomedical Engineering Institute, Capital Medical University, Beijing, China
      name:Department of Molecular Cardiology, Cleveland Clinic, Cleveland, USA

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