Unraveling the Hidden Heterogeneities of Breast Cancer Based on Functional miRNA Cluster
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{"title"=>"Unraveling the hidden heterogeneities of breast cancer based on functional miRNA cluster", "type"=>"journal", "authors"=>[{"first_name"=>"Li", "last_name"=>"Li", "scopus_author_id"=>"56306554800"}, {"first_name"=>"Chang", "last_name"=>"Liu", "scopus_author_id"=>"57191676774"}, {"first_name"=>"Fang", "last_name"=>"Wang", "scopus_author_id"=>"56544807700"}, {"first_name"=>"Wei", "last_name"=>"Miao", "scopus_author_id"=>"56156578700"}, {"first_name"=>"Jie", "last_name"=>"Zhang", "scopus_author_id"=>"56034174500"}, {"first_name"=>"Zhiqian", "last_name"=>"Kang", "scopus_author_id"=>"56157300000"}, {"first_name"=>"Yihan", "last_name"=>"Chen", "scopus_author_id"=>"35278344700"}, {"first_name"=>"Luying", "last_name"=>"Peng", "scopus_author_id"=>"7201574115"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84900417176", "doi"=>"10.1371/journal.pone.0087601", "issn"=>"19326203", "pui"=>"373070843", "sgr"=>"84900417176", "isbn"=>"1932-6203 (Electronic) 1932-6203 (Linking)", "pmid"=>"24498150"}, "id"=>"d369e318-cf99-35b5-89df-c7d4e0644f67", "abstract"=>"It has become increasingly clear that the current taxonomy of clinical phenotypes is mixed with molecular heterogeneity, which potentially affects the treatment effect for involved patients. Defining the hidden molecular-distinct diseases using modern large-scale genomic approaches is therefore useful for refining clinical practice and improving intervention strategies. Given that microRNA expression profiling has provided a powerful way to dissect hidden genetic heterogeneity for complex diseases, the aim of the study was to develop a bioinformatics approach that identifies microRNA features leading to the hidden subtyping of complex clinical phenotypes. The basic strategy of the proposed method was to identify optimal miRNA clusters by iteratively partitioning the sample and feature space using the two-ways super-paramagnetic clustering technique. We evaluated the obtained optimal miRNA cluster by determining the consistency of co-expression and the chromosome location among the within-cluster microRNAs, and concluded that the optimal miRNA cluster could lead to a natural partition of disease samples. We applied the proposed method to a publicly available microarray dataset of breast cancer patients that have notoriously heterogeneous phenotypes. We obtained a feature subset of 13 microRNAs that could classify the 71 breast cancer patients into five subtypes with significantly different five-year overall survival rates (45%, 82.4%, 70.6%, 100% and 60% respectively; p = 0.008). By building a multivariate Cox proportional-hazards prediction model for the feature subset, we identified has-miR-146b as one of the most significant predictor (p = 0.045; hazard ratios = 0.39). The proposed algorithm is a promising computational strategy for dissecting hidden genetic heterogeneity for complex diseases, and will be of value for improving cancer diagnosis and treatment.", "link"=>"http://www.mendeley.com/research/unraveling-hidden-heterogeneities-breast-cancer-based-functional-mirna-cluster", "reader_count"=>13, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>3, "Student > Postgraduate"=>1, "Student > Master"=>2, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>3, "Student > Postgraduate"=>1, "Student > Master"=>2, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>2, "Medicine and Dentistry"=>2, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}}, "reader_count_by_country"=>{"Egypt"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1368355"], "description"=>"<p>The graphic algorithm flow for the proposed SPC-based two-way clustering.</p>", "links"=>[], "tags"=>["Biochemistry", "Nucleic acids", "rna", "Computational biology", "genomics", "epigenomics", "Genome analysis tools", "microarrays", "genetics", "Heredity", "phenotypes", "Applied mathematics", "algorithms", "Obstetrics and gynecology", "breast cancer", "oncology", "Basic cancer research", "graphic", "algorithm", "spc-based", "two-way"], "article_id"=>919559, "categories"=>["Biological Sciences", "Mathematics", "Medicine"], "users"=>["Li Li", "Chang Liu", "Fang Wang", "Wei Miao", "Jie Zhang", "Zhiqian Kang", "Yihan Chen", "Luying Peng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087601.g001", "stats"=>{"downloads"=>1, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_graphic_algorithm_flow_for_the_proposed_SPC_based_two_way_clustering_/919559", "title"=>"The graphic algorithm flow for the proposed SPC-based two-way clustering.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-30 03:55:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1368358"], "description"=>"<p>In the figure, each microRNA corresponds to a row, and each breast cancer sample corresponds to column. The 71 breast cancer samples were divided into five subtypes (Subtype 1, Subtype 2, Subtype 3, Subtype 4 and Subtype 5). Red areas indicate increased expression, and green areas decreased expression. Each column represents a single breast cancer sample, and each row represents a single microRNA. The dendrogram at the top shows the degree to which each breast cancer subtype is related to the others with respect to microRNA expression.</p>", "links"=>[], "tags"=>["Biochemistry", "Nucleic acids", "rna", "Computational biology", "genomics", "epigenomics", "Genome analysis tools", "microarrays", "genetics", "Heredity", "phenotypes", "Applied mathematics", "algorithms", "Obstetrics and gynecology", "breast cancer", "oncology", "Basic cancer research", "partitions", "cancer"], "article_id"=>919562, "categories"=>["Biological Sciences", "Mathematics", "Medicine"], "users"=>["Li Li", "Chang Liu", "Fang Wang", "Wei Miao", "Jie Zhang", "Zhiqian Kang", "Yihan Chen", "Luying Peng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087601.g002", "stats"=>{"downloads"=>3, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_five_partitions_of_breast_cancer_were_identified_using_as_the_disease_feature_set_in_the_breast_cancer_dataset_/919562", "title"=>"The five partitions of breast cancer were identified using as the disease feature set in the breast cancer dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-30 03:55:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1368359"], "description"=>"<p>Survival curves for five subtypes of the breast cancer patients in the breast cancer dataset.</p>", "links"=>[], "tags"=>["Biochemistry", "Nucleic acids", "rna", "Computational biology", "genomics", "epigenomics", "Genome analysis tools", "microarrays", "genetics", "Heredity", "phenotypes", "Applied mathematics", "algorithms", "Obstetrics and gynecology", "breast cancer", "oncology", "Basic cancer research", "curves", "subtypes", "cancer", "patients"], "article_id"=>919563, "categories"=>["Biological Sciences", "Mathematics", "Medicine"], "users"=>["Li Li", "Chang Liu", "Fang Wang", "Wei Miao", "Jie Zhang", "Zhiqian Kang", "Yihan Chen", "Luying Peng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087601.g003", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Survival_curves_for_five_subtypes_of_the_breast_cancer_patients_in_the_breast_cancer_dataset_/919563", "title"=>"Survival curves for five subtypes of the breast cancer patients in the breast cancer dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-30 03:55:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1368360"], "description"=>"<p>Multivariate Cox proportional-hazards analysis based on the signature microRNAs relevant to survival time.</p>", "links"=>[], "tags"=>["Biochemistry", "Nucleic acids", "rna", "Computational biology", "genomics", "epigenomics", "Genome analysis tools", "microarrays", "genetics", "Heredity", "phenotypes", "Applied mathematics", "algorithms", "Obstetrics and gynecology", "breast cancer", "oncology", "Basic cancer research", "cox", "proportional-hazards", "micrornas"], "article_id"=>919564, "categories"=>["Biological Sciences", "Mathematics", "Medicine"], "users"=>["Li Li", "Chang Liu", "Fang Wang", "Wei Miao", "Jie Zhang", "Zhiqian Kang", "Yihan Chen", "Luying Peng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087601.t002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multivariate_Cox_proportional_hazards_analysis_based_on_the_signature_microRNAs_relevant_to_survival_time_/919564", "title"=>"Multivariate Cox proportional-hazards analysis based on the signature microRNAs relevant to survival time.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-30 03:55:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1368361"], "description"=>"<p>miRNA clusters using CTWC</p>", "links"=>[], "tags"=>["Biochemistry", "Nucleic acids", "rna", "Computational biology", "genomics", "epigenomics", "Genome analysis tools", "microarrays", "genetics", "Heredity", "phenotypes", "Applied mathematics", "algorithms", "Obstetrics and gynecology", "breast cancer", "oncology", "Basic cancer research", "clusters"], "article_id"=>919565, "categories"=>["Biological Sciences", "Mathematics", "Medicine"], "users"=>["Li Li", "Chang Liu", "Fang Wang", "Wei Miao", "Jie Zhang", "Zhiqian Kang", "Yihan Chen", "Luying Peng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087601.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_miRNA_clusters_using_CTWC_/919565", "title"=>"miRNA clusters using CTWC", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-30 03:55:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1368362", "https://ndownloader.figshare.com/files/1368363"], "description"=>"<div><p>It has become increasingly clear that the current taxonomy of clinical phenotypes is mixed with molecular heterogeneity, which potentially affects the treatment effect for involved patients. Defining the hidden molecular-distinct diseases using modern large-scale genomic approaches is therefore useful for refining clinical practice and improving intervention strategies. Given that microRNA expression profiling has provided a powerful way to dissect hidden genetic heterogeneity for complex diseases, the aim of the study was to develop a bioinformatics approach that identifies microRNA features leading to the hidden subtyping of complex clinical phenotypes. The basic strategy of the proposed method was to identify optimal miRNA clusters by iteratively partitioning the sample and feature space using the two-ways super-paramagnetic clustering technique. We evaluated the obtained optimal miRNA cluster by determining the consistency of co-expression and the chromosome location among the within-cluster microRNAs, and concluded that the optimal miRNA cluster could lead to a natural partition of disease samples. We applied the proposed method to a publicly available microarray dataset of breast cancer patients that have notoriously heterogeneous phenotypes. We obtained a feature subset of 13 microRNAs that could classify the 71 breast cancer patients into five subtypes with significantly different five-year overall survival rates (45%, 82.4%, 70.6%, 100% and 60% respectively; <i>p</i> = 0.008). By building a multivariate Cox proportional-hazards prediction model for the feature subset, we identified has-miR-146b as one of the most significant predictor (<i>p</i> = 0.045; hazard ratios = 0.39). The proposed algorithm is a promising computational strategy for dissecting hidden genetic heterogeneity for complex diseases, and will be of value for improving cancer diagnosis and treatment.</p></div>", "links"=>[], "tags"=>["Biochemistry", "Nucleic acids", "rna", "Computational biology", "genomics", "epigenomics", "Genome analysis tools", "microarrays", "genetics", "Heredity", "phenotypes", "Applied mathematics", "algorithms", "Obstetrics and gynecology", "breast cancer", "oncology", "Basic cancer research", "heterogeneities", "cancer", "mirna"], "article_id"=>919566, "categories"=>["Biological Sciences", "Mathematics", "Medicine"], "users"=>["Li Li", "Chang Liu", "Fang Wang", "Wei Miao", "Jie Zhang", "Zhiqian Kang", "Yihan Chen", "Luying Peng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0087601.s001", "https://dx.doi.org/10.1371/journal.pone.0087601.s002"], "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unraveling_the_Hidden_Heterogeneities_of_Breast_Cancer_Based_on_Functional_miRNA_Cluster_/919566", "title"=>"Unraveling the Hidden Heterogeneities of Breast Cancer Based on Functional miRNA Cluster", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-30 03:55:19"}

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