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{"title"=>"Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients", "type"=>"journal", "authors"=>[{"first_name"=>"Jie", "last_name"=>"Lu", "scopus_author_id"=>"57191860437"}, {"first_name"=>"Matthew C.", "last_name"=>"Cowperthwaite", "scopus_author_id"=>"6601994536"}, {"first_name"=>"Mark G.", "last_name"=>"Burnett", "scopus_author_id"=>"7004960272"}, {"first_name"=>"Max", "last_name"=>"Shpak", "scopus_author_id"=>"6701667917"}], "year"=>2016, "source"=>"PloS one", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-85016129383", "pui"=>"615044698", "doi"=>"10.1371/journal.pone.0154313", "sgr"=>"85016129383", "pmid"=>"27124395"}, "id"=>"be1eaad5-d685-3c7e-9a13-d3124ed3ca44", "abstract"=>"Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.", "link"=>"http://www.mendeley.com/research/molecular-predictors-longterm-survival-glioblastoma-multiforme-patients-1", "reader_count"=>20, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Other"=>3, "Student > Master"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Other"=>3, "Student > Master"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>2, "Agricultural and Biological Sciences"=>6, "Neuroscience"=>3, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Netherlands"=>1}, "group_count"=>0}

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• http://doi.org/10.1186/s12885-017-3148-1
• http://doi.org/10.3390/ijms17071029
• http://doi.org/10.1016/j.clineuro.2017.05.015
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• http://doi.org/10.3390/cancers12071817
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• http://doi.org/10.1534/g3.118.200391
• http://doi.org/10.1186/s12859-018-2500-z
• http://doi.org/10.1097/MD.0000000000023890
• http://doi.org/10.1093/jnen/nlaa059
• http://doi.org/10.1093/neuros/nyw174
• http://doi.org/10.3390/cells9112429
• http://doi.org/10.1016/j.thromres.2018.05.001
• http://doi.org/10.1016/j.jocn.2019.08.017
• http://doi.org/10.21294/1814-4861-2019-18-3-34-44
• http://doi.org/10.1093/jnen/nlz025
• http://doi.org/10.1186/s12920-019-0479-6
• http://doi.org/10.1186/s40478-019-0744-0

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/5046808"], "description"=>"<p>Flow chart with a schematic of the data analysis pipeline used in this study.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215299, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Flow_chart_with_a_schematic_of_the_data_analysis_pipeline_used_in_this_study_/3215299", "title"=>"Flow chart with a schematic of the data analysis pipeline used in this study.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5046997"], "description"=>"<p>Hierarchical clustering (HC) on the DNA methylation levels of GBM and LGG (N = 23233) was used to classify the samples. The row dendrogram (clustering among samples) is based on Pearson correlation coefficients and the column dendrogram on Spearman correlation coefficients. In the row-side color bars of <i>IDH</i> status and LGG history, red indicates <i>IDH1</i>⁺ or history of LGG diagnosis; grey indicates <i>IDH</i>1^- or no history of LGG diagnosis; white indicates no data available for the sample.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215437, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.g006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Heatmap_of_the_DNA_methylation_levels_in_LTS_GBM_non-LTS_GBM_and_LGG_samples_N_383_/3215437", "title"=>"Heatmap of the DNA methylation levels in LTS GBM, non-LTS GBM and LGG samples (N = 383).", "pos_in_sequence"=>7, "defined_type"=>1, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5046898"], "description"=>"<p>Hierarchical clustering (HC) on the expression levels of DEGs between GBM and LGG (N = 491) was used to classify the samples, with a row dendrogram (clustering of samples) based on Pearson correlation coefficient, the column dendrogram on a Spearman correlation coefficient. In the row-side color bars of <i>IDH</i> status and LGG history, red indicates <i>IDH1</i>⁺ or history of LGG diagnosis; grey indicates <i>IDH</i>1^- and no history of LGG diagnosis, respectively; white indicates that no data is available for the sample.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215374, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Heatmap_of_the_gene_expression_levels_in_LTS_GBM_non-LTS_GBM_and_LGG_samples_N_383_genes_/3215374", "title"=>"Heatmap of the gene expression levels in LTS GBM, non-LTS GBM and LGG samples (N = 383 genes).", "pos_in_sequence"=>5, "defined_type"=>1, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047306"], "description"=>"<p>Moran's I for each binary category (e.g. LTS vs. non-LTS).</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215704, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t009", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Moran_s_I_for_each_binary_category_e_g_LTS_vs_non-LTS_/3215704", "title"=>"Moran's I for each binary category (e.g. LTS vs. non-LTS).", "pos_in_sequence"=>16, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047216"], "description"=>"<p>Prediction performance (area under curve, AUC) of integrative models.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215635, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Prediction_performance_area_under_curve_AUC_of_integrative_models_/3215635", "title"=>"Prediction performance (area under curve, AUC) of integrative models.", "pos_in_sequence"=>13, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047147"], "description"=>"<p>List of significant predictor genes in LLR using single classes of molecular data.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215572, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/List_of_significant_predictor_genes_in_LLR_using_single_classes_of_molecular_data_/3215572", "title"=>"List of significant predictor genes in LLR using single classes of molecular data.", "pos_in_sequence"=>11, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5046832"], "description"=>"<p>a. Kaplan-Meier plot of overall survival analysis of 591 GBM patients. The vertical line indicates the 3-year (1095 days) cutoff for LTS used in following analyses. The horizontal dashed line indicates 7.6% LTS patients corresponding to the cutoff. b. Histogram of survival time (in days) showing that the distribution of survival time is unimodal.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215320, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Survival_time_analyses_of_GBM_patients_/3215320", "title"=>"Survival time analyses of GBM patients.", "pos_in_sequence"=>3, "defined_type"=>1, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047078"], "description"=>"<p>Summary of the 6 types of molecular data and their platforms used for this study.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215515, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Summary_of_the_6_types_of_molecular_data_and_their_platforms_used_for_this_study_/3215515", "title"=>"Summary of the 6 types of molecular data and their platforms used for this study.", "pos_in_sequence"=>9, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5046943"], "description"=>"<p>PCA analyses were performed on a. All probes (<i>N</i> = 23233). b. ULR predictor genes (<i>N</i> = 38). c. LLR predictor genes (<i>N</i> = 43).</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215392, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Scatterplot_of_the_first_two_principal_components_of_the_DNA_methylation_data_/3215392", "title"=>"Scatterplot of the first two principal components of the DNA methylation data.", "pos_in_sequence"=>6, "defined_type"=>1, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5046631", "https://ndownloader.figshare.com/files/5046652", "https://ndownloader.figshare.com/files/5046667", "https://ndownloader.figshare.com/files/5046688", "https://ndownloader.figshare.com/files/5046706", "https://ndownloader.figshare.com/files/5046721", "https://ndownloader.figshare.com/files/5046745", "https://ndownloader.figshare.com/files/5046766"], "description"=>"<div><p>Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.</p></div>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215185, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0154313.s001", "https://dx.doi.org/10.1371/journal.pone.0154313.s002", "https://dx.doi.org/10.1371/journal.pone.0154313.s003", "https://dx.doi.org/10.1371/journal.pone.0154313.s004", "https://dx.doi.org/10.1371/journal.pone.0154313.s005", "https://dx.doi.org/10.1371/journal.pone.0154313.s006", "https://dx.doi.org/10.1371/journal.pone.0154313.s007", "https://dx.doi.org/10.1371/journal.pone.0154313.s008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Molecular_Predictors_of_Long-Term_Survival_in_Glioblastoma_Multiforme_Patients/3215185", "title"=>"Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients", "pos_in_sequence"=>1, "defined_type"=>4, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047093"], "description"=>"<p>Partial regression coefficients for logistic regression model for LTS against clinical and demographical information.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215530, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Partial_regression_coefficients_for_logistic_regression_model_for_LTS_against_clinical_and_demographical_information_/3215530", "title"=>"Partial regression coefficients for logistic regression model for LTS against clinical and demographical information.", "pos_in_sequence"=>10, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047255"], "description"=>"<p>List of significant predictor genes in integrative LLR models, with various combinations of data classes.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215665, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t007", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/List_of_significant_predictor_genes_in_integrative_LLR_models_with_various_combinations_of_data_classes_/3215665", "title"=>"List of significant predictor genes in integrative LLR models, with various combinations of data classes.", "pos_in_sequence"=>14, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047039"], "description"=>"<p>Summary of clinical and demographical information of the TCGA patient cohort used for this study.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215479, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Summary_of_clinical_and_demographical_information_of_the_TCGA_patient_cohort_used_for_this_study_/3215479", "title"=>"Summary of clinical and demographical information of the TCGA patient cohort used for this study.", "pos_in_sequence"=>8, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047183"], "description"=>"<p>Prediction performance of individual molecular type under LLR, as measured by AUC.</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215608, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Prediction_performance_of_individual_molecular_type_under_LLR_as_measured_by_AUC_/3215608", "title"=>"Prediction performance of individual molecular type under LLR, as measured by AUC.", "pos_in_sequence"=>12, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5046868"], "description"=>"<p>PCA analyses were performed on a. DEGs between GBM and LGG (N = 491 genes). b. DEGs between GBM and normal controls (N = 4801). c. ULR predictor genes (N = 94). d. LLR predictor genes (N = 38).</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215353, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Scatterplot_of_the_first_two_principal_components_of_gene_expression_data_/3215353", "title"=>"Scatterplot of the first two principal components of gene expression data.", "pos_in_sequence"=>4, "defined_type"=>1, "published_date"=>"2016-04-28 08:45:28"}
• {"files"=>["https://ndownloader.figshare.com/files/5047273"], "description"=>"<p>Moran's I for each binary category (e.g. LTS vs. non-LTS).</p>", "links"=>[], "tags"=>["miRNA expression data sets", "GBM patients", "GBM LTS", "LTS GBM", "CNV", "PCA", "gene expression profiles", "copy number variation", "gene expression", "point mutation", "TCGA", "DNA methylation", "regression models", "AUC", "Cancer Genome Atlas", "cluster analysis", "LGG", "Glioblastoma Multiforme Patients Glioblastoma multiforme"], "article_id"=>3215677, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Developmental Biology", "Cancer"], "users"=>["Jie Lu", "Matthew C. Cowperthwaite", "Mark G. Burnett", "Max Shpak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154313.t008", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Moran_s_I_for_each_binary_category_e_g_LTS_vs_non-LTS_/3215677", "title"=>"Moran's I for each binary category (e.g. LTS vs. non-LTS).", "pos_in_sequence"=>15, "defined_type"=>3, "published_date"=>"2016-04-28 08:45:28"}

PMC Usage Stats | Further Information

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• {"unique-ip"=>"19", "full-text"=>"16", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
• {"unique-ip"=>"31", "full-text"=>"36", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"1", "year"=>"2018", "month"=>"12"}
• {"unique-ip"=>"20", "full-text"=>"24", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
• {"unique-ip"=>"22", "full-text"=>"22", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
• {"unique-ip"=>"24", "full-text"=>"23", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
• {"unique-ip"=>"13", "full-text"=>"13", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
• {"unique-ip"=>"13", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
• {"unique-ip"=>"12", "full-text"=>"11", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
• {"unique-ip"=>"19", "full-text"=>"19", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"10", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
• {"unique-ip"=>"10", "full-text"=>"6", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
• {"unique-ip"=>"19", "full-text"=>"17", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
• {"unique-ip"=>"15", "full-text"=>"15", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"16", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
• {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
• {"unique-ip"=>"14", "full-text"=>"12", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
• {"unique-ip"=>"10", "full-text"=>"12", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
• {"unique-ip"=>"17", "full-text"=>"9", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
• {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
• {"unique-ip"=>"10", "full-text"=>"15", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
• {"unique-ip"=>"11", "full-text"=>"9", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
• {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}

Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}

F1000Prime