Genomic Copy Number Variations in the Genomes of Leukocytes Predict Prostate Cancer Clinical Outcomes
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{"title"=>"Genomic copy number variations in the genomes of leukocytes predict prostate cancer clinical outcomes", "type"=>"journal", "authors"=>[{"first_name"=>"Yan P.", "last_name"=>"Yu", "scopus_author_id"=>"35308177000"}, {"first_name"=>"Silvia", "last_name"=>"Liu", "scopus_author_id"=>"56508538000"}, {"first_name"=>"Zhiguang", "last_name"=>"Huo", "scopus_author_id"=>"55386206500"}, {"first_name"=>"Amantha", "last_name"=>"Martin", "scopus_author_id"=>"56688898300"}, {"first_name"=>"Joel B.", "last_name"=>"Nelson", "scopus_author_id"=>"7404796366"}, {"first_name"=>"George C.", "last_name"=>"Tseng", "scopus_author_id"=>"7006468940"}, {"first_name"=>"Jian Hua", "last_name"=>"Luo", "scopus_author_id"=>"7404182492"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84943168064", "pui"=>"606195464", "doi"=>"10.1371/journal.pone.0135982", "sgr"=>"84943168064", "pmid"=>"26295840"}, "id"=>"6a78349d-6cc3-386f-a887-d693dbc255be", "abstract"=>"Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.", "link"=>"http://www.mendeley.com/research/genomic-copy-number-variations-genomes-leukocytes-predict-prostate-cancer-clinical-outcomes", "reader_count"=>8, "reader_count_by_academic_status"=>{"Researcher"=>1, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>2, "Student > Master"=>1, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Researcher"=>1, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>2, "Student > Master"=>1, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>1, "Medicine and Dentistry"=>2, "Agricultural and Biological Sciences"=>1, "Unspecified"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2219694"], "description"=>"<p>LSR derived from leukocyte genome CNV predicts PSADT 4 months or less. ROC analysis using LSRs derived from leukocyte CNVs as a prediction parameter (red) to predict PSADT 4 months or less, versus Nomogram (blue), Gleason’s grade (green) and the status of 8 fusion transcripts[<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135982#pone.0135982.ref014\" target=\"_blank\">14</a>] (yellow). Samples were analyzed by the same procedure as <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135982#pone.0135982.g003\" target=\"_blank\">Fig 3</a>. (B) Combination of LSR (L), Gleason’s grade (G), Nomogram (N) and the status of fusion transcripts (F) to predict prostate cancer recurrent PSADT 4 months or less. ROC analysis of a model combining LSR, fusion transcripts, Nomogram and Gleason’s grade using LDA is indicated by black. ROC analysis of a model combining fusion transcripts, Nomogram and Gleason’s grade using LDA is indicated by red. ROC analysis of a model combining LSR, fusion transcripts and Gleason’s grade using LDA is indicated by blue. ROC analysis of a model combining LSR, fusion transcripts and Nomogram using LDA is indicated by green. ROC analysis of a model combining LSR, Nomogram and Gleason’s grade is indicated by yellow.</p>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516065, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135982.g005", "stats"=>{"downloads"=>2, "page_views"=>31, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_LSR_of_genome_CNV_from_leukocytes_to_predict_prostate_cancer_recurrence_with_short_PSADT_/1516065", "title"=>"LSR of genome CNV from leukocytes to predict prostate cancer recurrence with short PSADT.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-21 02:48:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/2219697"], "description"=>"<p>Kaplan-Meier analysis on patients predicted by LSR based on CNV of patients’ leukocytes as likely recurrent and having PSADT 4 months or less versus likely non-recurrent or recurrent but having PSADT 15 months or more (upper left). Similar survival analyses were also performed on case segregations based on Gleason’s grades (upper middle), Nomogram probability (upper right), the status of 8 fusion transcripts (lower left), or a model by combining LSR, Nomogram and fusion transcript status using LDA (lower middle), or a model by combining LSR, Nomogram, Gleason grade and fusion transcript status using LDA (lower right). Number of samples analyzed and p values are indicated.</p>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516067, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135982.g006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_CNVs_from_leukocytes_predicting_short_PSADT_correlated_with_lower_PSA_free_survival_/1516067", "title"=>"Genome CNVs from leukocytes predicting short PSADT correlated with lower PSA-free survival.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-21 02:48:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/2219698"], "description"=>"<p>L-LSR; N-Nomogram; F-fusion transcript status; G-Gleason grade.</p><p>L+N+F: LDA model to combine LSR, Nomogram and fusion transcript status</p><p>L+N+G: LDA model to combine LSR, Nomogram and Gleason grade</p><p>N+F+G: LDA model to combine Nomogram, fusion transcript status and Gleason grade</p><p>L+N+F+G: LDA model to combine LSR, Nomogram, fusion transcript status and Gleason grade.</p><p>The results represent the average of the analyses on 10 random equal splits of training and testing results.</p><p>Prediction of prostate cancer recurrence based on leukocyte LSR, Gleason, Nomogram and fusion transcript status.</p>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516069, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135982.t001", "stats"=>{"downloads"=>10, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_of_prostate_cancer_recurrence_based_on_leukocyte_LSR_Gleason_Nomogram_and_fusion_transcript_status_/1516069", "title"=>"Prediction of prostate cancer recurrence based on leukocyte LSR, Gleason, Nomogram and fusion transcript status.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-21 02:48:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/2219699"], "description"=>"<p>L-LSR; N-Nomogram; F-fusion transcript status; G-Gleason grade.</p><p>L+N+F: LDA model to combine LSR, Nomogram and fusion transcript status</p><p>L+N+G: LDA model to combine LSR, Nomogram and Gleason grade</p><p>N+F+G: LDA model to combine Nomogram, fusion transcript status and Gleason grade</p><p>L+N+F+G: LDA model to combine LSR, Nomogram, fusion transcript status and Gleason grade.</p><p>The results represent the average of the analyses on 10 random equal splits of training and testing results.</p><p>Prediction of prostate cancer recurrent PSADT≤4 months based on leukocyte LSR, Gleason, Nomogram and fusion transcript status.</p>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516070, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135982.t002", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_of_prostate_cancer_recurrent_PSADT_8804_4_months_based_on_leukocyte_LSR_Gleason_Nomogram_and_fusion_transcript_status_/1516070", "title"=>"Prediction of prostate cancer recurrent PSADT≤4 months based on leukocyte LSR, Gleason, Nomogram and fusion transcript status.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-21 02:48:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/2219712", "https://ndownloader.figshare.com/files/2219713", "https://ndownloader.figshare.com/files/2219714", "https://ndownloader.figshare.com/files/2219715", "https://ndownloader.figshare.com/files/2219716", "https://ndownloader.figshare.com/files/2219717", "https://ndownloader.figshare.com/files/2219718", "https://ndownloader.figshare.com/files/2219719", "https://ndownloader.figshare.com/files/2219720", "https://ndownloader.figshare.com/files/2219721", "https://ndownloader.figshare.com/files/2219722", "https://ndownloader.figshare.com/files/2219723", "https://ndownloader.figshare.com/files/2219724", "https://ndownloader.figshare.com/files/2219725", "https://ndownloader.figshare.com/files/2219726", "https://ndownloader.figshare.com/files/2219727", "https://ndownloader.figshare.com/files/2219728"], "description"=>"<div><p>Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.</p></div>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516078, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0135982.s001", "https://dx.doi.org/10.1371/journal.pone.0135982.s002", "https://dx.doi.org/10.1371/journal.pone.0135982.s003", "https://dx.doi.org/10.1371/journal.pone.0135982.s004", "https://dx.doi.org/10.1371/journal.pone.0135982.s005", "https://dx.doi.org/10.1371/journal.pone.0135982.s006", "https://dx.doi.org/10.1371/journal.pone.0135982.s007", "https://dx.doi.org/10.1371/journal.pone.0135982.s008", "https://dx.doi.org/10.1371/journal.pone.0135982.s009", "https://dx.doi.org/10.1371/journal.pone.0135982.s010", "https://dx.doi.org/10.1371/journal.pone.0135982.s011", "https://dx.doi.org/10.1371/journal.pone.0135982.s012", "https://dx.doi.org/10.1371/journal.pone.0135982.s013", "https://dx.doi.org/10.1371/journal.pone.0135982.s014", "https://dx.doi.org/10.1371/journal.pone.0135982.s015", "https://dx.doi.org/10.1371/journal.pone.0135982.s016", "https://dx.doi.org/10.1371/journal.pone.0135982.s017"], "stats"=>{"downloads"=>14, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genomic_Copy_Number_Variations_in_the_Genomes_of_Leukocytes_Predict_Prostate_Cancer_Clinical_Outcomes_/1516078", "title"=>"Genomic Copy Number Variations in the Genomes of Leukocytes Predict Prostate Cancer Clinical Outcomes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-08-21 02:48:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/2219682"], "description"=>"<p>(A) Histogram of frequency of amplification (red) or deletion (blue) of genome sequences of leukocytes (upper panel, n = 273) from prostate cancer patients. (B) Manhattan plots of p-values in association with prostate cancer recurrence of each gene CNV from leukocytes.</p>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516058, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135982.g001", "stats"=>{"downloads"=>4, "page_views"=>68, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Copy_number_variations_CNV_in_blood_and_prostate_cancer_from_prostate_cancer_patients_/1516058", "title"=>"Copy number variations (CNV) in blood and prostate cancer from prostate cancer patients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-21 02:48:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/2219689"], "description"=>"<p>(A) Schematic diagram of LSR model of leukocyte CNV. (B) LSRs from leukocytes are associated with aggressive prostate cancer recurrence behavior. Upper panel: Correlation of LSRs from leukocyte genomes with prostate cancers that were recurrent; Lower panel: Correlation of LSRs from leukocyte genomes with prostate cancers that were non-recurrent 90 months after radical prostatectomy. (C) LSRs from leukocytes are associated with short PSADT. Upper panel: Correlation of LSRs from leukocyte genomes with prostate cancers that had recurrent serum prostate specific antigen doubling time (PSADT) 4 months or less; Lower panel: Correlation of LSRs from leukocyte genomes with prostate cancers that were not recurrent or recurrent but having PSADT 15 months or more.</p>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516060, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135982.g002", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Large_size_ratio_LSR_of_CNVs_from_leukocytes_from_prostate_cancer_patients_are_correlated_with_aggressive_behavior_of_prostate_cancer_/1516060", "title"=>"Large size ratio (LSR) of CNVs from leukocytes from prostate cancer patients are correlated with aggressive behavior of prostate cancer.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-21 02:48:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/2219690"], "description"=>"<p>(A) LSR derived from leukocyte genome CNV predicts prostate cancer recurrence. Receiver operating curve (ROC) analyses using LSRs derived from leukocyte CNVs as prediction parameter (red) to predict prostate cancer recurrence, versus Nomogram (blue), Gleason’s grade (green) and the status of 8 fusion transcripts[<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135982#pone.0135982.ref014\" target=\"_blank\">14</a>] (yellow). The samples were equally split randomly into training and testing sets 10 times. The ROC analysis represents the results from the most representative split. (B) Combination of LSR (L), Gleason’s grade (G), Nomogram (N) and the status of fusion transcripts (F) to predict prostate cancer recurrence. ROC analysis of a model combining LSR, fusion transcripts, Nomogram and Gleason’s grade using LDA is indicated by black. ROC analysis of a model combining fusion transcripts, Nomogram and Gleason’s grade using LDA is indicated by red. ROC analysis of a model combining LSR, fusion transcripts and Gleason’s grade using LDA is indicated by blue. ROC analysis of a model combining LSR, fusion transcripts and Nomogram using LDA is indicated by green. ROC analysis of a model combining LSR, Nomogram and Gleason’s grade is indicated by yellow. Similar random splits of training and testing data sets were performed as of (A).</p>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516061, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135982.g003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_LSR_of_genome_CNV_from_leukocytes_to_predict_prostate_cancer_recurrence_/1516061", "title"=>"LSR of genome CNV from leukocytes to predict prostate cancer recurrence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-21 02:48:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/2219692"], "description"=>"<p>Kaplan-Meier analysis on patients predicted by LSR based on CNV of patients’ leukocytes as likely recurrent versus likely non-recurrent (upper left). Similar survival analyses were also performed on case segregations based on Gleason’s grades (upper middle), Nomogram probability (upper right), the status of 8 fusion transcripts (lower left), or a model by combining LSR, Nomogram and fusion transcript status using LDA (lower middle), or a model by combining LSR, Nomogram, Gleason grade and fusion transcript status using LDA (lower right). Number of samples analyzed and p values are indicated.</p>", "links"=>[], "tags"=>["prostate cancer outcome prediction model", "fusion transcripts", "copy number variations", "152 CNV fragments", "sensitivity", "genome copy number analysis", "prostate cancer recurrence", "Affymetrix SNP 6.0 chip", "prediction rate", "leukocyte prediction model", "Genomic copy number variations", "specificity", "Leukocytes Predict Prostate Cancer Clinical Outcomes", "273 prostate cancer patients", "leukocyte genomes", "prediction model", "prostate cancer patients", "prostate cancer aggressiveness"], "article_id"=>1516063, "categories"=>["Uncategorised"], "users"=>["Yan P. Yu", "Silvia Liu", "Zhiguang Huo", "Amantha Martin", "Joel B. Nelson", "George C. Tseng", "Jian-Hua Luo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135982.g004", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Large_LSRs_of_genome_CNVs_from_leukocytes_correlated_with_lower_PSA_free_survival_/1516063", "title"=>"Large LSRs of genome CNVs from leukocytes correlated with lower PSA-free survival.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-21 02:48:53"}

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

Relative Metric

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