Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer
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{"title"=>"Circulating microRNAs as non-invasive biomarkers for early detection of non-small-cell lung cancer", "type"=>"journal", "authors"=>[{"first_name"=>"Magdalena B.", "last_name"=>"Wozniak", "scopus_author_id"=>"35742523900"}, {"first_name"=>"Ghislaine", "last_name"=>"Scelo", "scopus_author_id"=>"9334611100"}, {"first_name"=>"David C.", "last_name"=>"Muller", "scopus_author_id"=>"8050408600"}, {"first_name"=>"Anush", "last_name"=>"Mukeria", "scopus_author_id"=>"6603026158"}, {"first_name"=>"David", "last_name"=>"Zaridze", "scopus_author_id"=>"7005676681"}, {"first_name"=>"Paul", "last_name"=>"Brennan", "scopus_author_id"=>"7402304490"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84930640607", "doi"=>"10.1371/journal.pone.0125026", "pui"=>"604611982", "pmid"=>"25965386", "scopus"=>"2-s2.0-84930640607", "issn"=>"19326203"}, "id"=>"608e978e-76ae-3d0b-a2e7-c980c8b29740", "abstract"=>"BACKGROUND: Detection of lung cancer at an early stage by sensitive screening tests could be an important strategy to improving prognosis. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of lung cancer.\\n\\nMATERIAL AND METHODS: Plasma samples from 100 early stage (I to IIIA) non-small-cell lung cancer (NSCLC) patients and 100 non-cancer controls were screened for 754 circulating microRNAs via qRT-PCR, using TaqMan MicroRNA Arrays. Logistic regression with a lasso penalty was used to select a panel of microRNAs that discriminate between cases and controls. Internal validation of model discrimination was conducted by calculating the bootstrap optimism-corrected AUC for the selected model.\\n\\nRESULTS: We identified a panel of 24 microRNAs with optimum classification performance. The combination of these 24 microRNAs alone could discriminate lung cancer cases from non-cancer controls with an AUC of 0.92 (95% CI: 0.87-0.95). This classification improved to an AUC of 0.94 (95% CI: 0.90-0.97) following addition of sex, age and smoking status to the model. Internal validation of the model suggests that the discriminatory power of the panel will be high when applied to independent samples with a corrected AUC of 0.78 for the 24-miRNA panel alone.\\n\\nCONCLUSION: Our 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors.", "link"=>"http://www.mendeley.com/research/circulating-micrornas-noninvasive-biomarkers-early-detection-nonsmallcell-lung-cancer-2", "reader_count"=>36, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Researcher"=>8, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>3, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Researcher"=>8, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>3, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>10, "Materials Science"=>1, "Agricultural and Biological Sciences"=>10, "Medicine and Dentistry"=>9, "Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>10}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2064603"], "description"=>"<p><sup>a</sup> Model containing the 24-miRNA panel (continuous, normalized Ct values).</p><p><sup>b</sup> Model containing sex (Male, Female), age at interview (continuous), smoking status (Never, Former, Current) and the 24-miRNA panel (continuous, normalized Ct values).</p><p>Abbreviations: OR, odds ratio; CI, confidence interval.</p><p>Logistic regression prediction model with the 24-microRNA panel for patients with lung cancer vs controls in the IARC case-control study (Moscow, Russia, 2006–2012).</p>", "links"=>[], "tags"=>["24 microRNAs", "iiia", "Taqman MicroRNA Arrays", "panel", "lung cancer prediction", "auc", "nsclc", "lung cancer cases", "ci", "Internal validation", "model"], "article_id"=>1412169, "categories"=>["Biological Sciences"], "users"=>["Magdalena B. Wozniak", "Ghislaine Scelo", "David C. Muller", "Anush Mukeria", "David Zaridze", "Paul Brennan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125026.t003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Logistic_regression_prediction_model_with_the_24_microRNA_panel_for_patients_with_lung_cancer_vs_controls_in_the_IARC_case_control_study_Moscow_Russia_2006_8211_2012_/1412169", "title"=>"Logistic regression prediction model with the 24-microRNA panel for patients with lung cancer vs controls in the IARC case-control study (Moscow, Russia, 2006–2012).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-05-12 03:28:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/2064601"], "description"=>"<p><sup>a</sup> p value calculated using χ<sup>2</sup> test for categorical variables and Student’s t-test for continuous variables.</p><p>Select baseline characteristics of the study population (lung cancer cases and controls) recruited in the IARC case-control study (Moscow, Russia, 2006–2012).</p>", "links"=>[], "tags"=>["24 microRNAs", "iiia", "Taqman MicroRNA Arrays", "panel", "lung cancer prediction", "auc", "nsclc", "lung cancer cases", "ci", "Internal validation", "model"], "article_id"=>1412167, "categories"=>["Biological Sciences"], "users"=>["Magdalena B. Wozniak", "Ghislaine Scelo", "David C. Muller", "Anush Mukeria", "David Zaridze", "Paul Brennan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125026.t001", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Select_baseline_characteristics_of_the_study_population_lung_cancer_cases_and_controls_recruited_in_the_IARC_case_control_study_Moscow_Russia_2006_8211_2012_/1412167", "title"=>"Select baseline characteristics of the study population (lung cancer cases and controls) recruited in the IARC case-control study (Moscow, Russia, 2006–2012).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-05-12 03:28:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/2064602"], "description"=>"<p><sup>a</sup> Limma analysis non-adjusted p-value.</p><p><sup>b</sup> p-value corrected for multiple testing (Benjamini-Holm method).</p><p>FC: fold change (> 1 increased; < 1 decreased expression in lung cancer patients vs. non-cancer controls).</p><p>Complete list of significant differentially expressed miRNA (p-value < 0.05) in lung cancer patients as compared with controls in the IARC case-control study (Moscow, Russia, 2006–2012)—global miRNA expression profiling using TaqMan Human MicroRNA Array A + B Card Set (v3.0).</p>", "links"=>[], "tags"=>["24 microRNAs", "iiia", "Taqman MicroRNA Arrays", "panel", "lung cancer prediction", "auc", "nsclc", "lung cancer cases", "ci", "Internal validation", "model"], "article_id"=>1412168, "categories"=>["Biological Sciences"], "users"=>["Magdalena B. Wozniak", "Ghislaine Scelo", "David C. Muller", "Anush Mukeria", "David Zaridze", "Paul Brennan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125026.t002", "stats"=>{"downloads"=>5, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Complete_list_of_significant_differentially_expressed_miRNA_p_value_lt_0_05_in_lung_cancer_patients_as_compared_with_controls_in_the_IARC_case_control_study_Moscow_Russia_2006_8211_2012_8212_global_miRNA_expression_profiling_using_TaqMan_Human_MicroRNA_A/1412168", "title"=>"Complete list of significant differentially expressed miRNA (p-value < 0.05) in lung cancer patients as compared with controls in the IARC case-control study (Moscow, Russia, 2006–2012)—global miRNA expression profiling using TaqMan Human MicroRNA Array A + B Card Set (v3.0).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-05-12 03:28:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/2064600"], "description"=>"<p>Evaluation of the performance of the model including age at recruitment, sex and smoking status (red) and a classifier including 24-miRNA panel, age at recruitment, sex and smoking status (blue) assessed using the area under Receiver Operating Characteristics (ROC) curves (AUCs) in the IARC case-control study (2006–2012).</p>", "links"=>[], "tags"=>["24 microRNAs", "iiia", "Taqman MicroRNA Arrays", "panel", "lung cancer prediction", "auc", "nsclc", "lung cancer cases", "ci", "Internal validation", "model"], "article_id"=>1412166, "categories"=>["Biological Sciences"], "users"=>["Magdalena B. Wozniak", "Ghislaine Scelo", "David C. Muller", "Anush Mukeria", "David Zaridze", "Paul Brennan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125026.g003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_the_performance_of_the_model_including_age_at_recruitment_sex_and_smoking_status_red_and_a_classifier_including_24_miRNA_panel_age_at_recruitment_sex_and_smoking_status_blue_assessed_using_the_area_under_Receiver_Operating_Characteristics_R/1412166", "title"=>"Evaluation of the performance of the model including age at recruitment, sex and smoking status (red) and a classifier including 24-miRNA panel, age at recruitment, sex and smoking status (blue) assessed using the area under Receiver Operating Characteristics (ROC) curves (AUCs) in the IARC case-control study (2006–2012).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-12 03:28:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/2064604", "https://ndownloader.figshare.com/files/2064605", "https://ndownloader.figshare.com/files/2064606", "https://ndownloader.figshare.com/files/2064607", "https://ndownloader.figshare.com/files/2064608", "https://ndownloader.figshare.com/files/2064609", "https://ndownloader.figshare.com/files/2064610", "https://ndownloader.figshare.com/files/2064611", "https://ndownloader.figshare.com/files/2064612", "https://ndownloader.figshare.com/files/2064613"], "description"=>"<div><p>Background</p><p>Detection of lung cancer at an early stage by sensitive screening tests could be an important strategy to improving prognosis. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of lung cancer.</p><p>Material and Methods</p><p>Plasma samples from 100 early stage (I to IIIA) non–small-cell lung cancer (NSCLC) patients and 100 non-cancer controls were screened for 754 circulating microRNAs via qRT-PCR, using TaqMan MicroRNA Arrays. Logistic regression with a lasso penalty was used to select a panel of microRNAs that discriminate between cases and controls. Internal validation of model discrimination was conducted by calculating the bootstrap optimism-corrected AUC for the selected model.</p><p>Results</p><p>We identified a panel of 24 microRNAs with optimum classification performance. The combination of these 24 microRNAs alone could discriminate lung cancer cases from non-cancer controls with an AUC of 0.92 (95% CI: 0.87-0.95). This classification improved to an AUC of 0.94 (95% CI: 0.90-0.97) following addition of sex, age and smoking status to the model. Internal validation of the model suggests that the discriminatory power of the panel will be high when applied to independent samples with a corrected AUC of 0.78 for the 24-miRNA panel alone.</p><p>Conclusion</p><p>Our 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors.</p></div>", "links"=>[], "tags"=>["24 microRNAs", "iiia", "Taqman MicroRNA Arrays", "panel", "lung cancer prediction", "auc", "nsclc", "lung cancer cases", "ci", "Internal validation", "model"], "article_id"=>1412170, "categories"=>["Biological Sciences"], "users"=>["Magdalena B. Wozniak", "Ghislaine Scelo", "David C. Muller", "Anush Mukeria", "David Zaridze", "Paul Brennan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0125026.s001", "https://dx.doi.org/10.1371/journal.pone.0125026.s002", "https://dx.doi.org/10.1371/journal.pone.0125026.s003", "https://dx.doi.org/10.1371/journal.pone.0125026.s004", "https://dx.doi.org/10.1371/journal.pone.0125026.s005", "https://dx.doi.org/10.1371/journal.pone.0125026.s006", "https://dx.doi.org/10.1371/journal.pone.0125026.s007", "https://dx.doi.org/10.1371/journal.pone.0125026.s008", "https://dx.doi.org/10.1371/journal.pone.0125026.s009", "https://dx.doi.org/10.1371/journal.pone.0125026.s010"], "stats"=>{"downloads"=>12, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Circulating_MicroRNAs_as_Non_Invasive_Biomarkers_for_Early_Detection_of_Non_Small_Cell_Lung_Cancer_/1412170", "title"=>"Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-05-12 03:28:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/2064599"], "description"=>"<p>Evaluation of the performance of the 24-miRNA classifier (left) and a classifier including the 24-miRNA panel, age at recruitment, sex and smoking status (right) assessed using area under Receiver Operating Characteristics (ROC) curves (AUCs) in the IARC case-control study (2006–2012).</p>", "links"=>[], "tags"=>["24 microRNAs", "iiia", "Taqman MicroRNA Arrays", "panel", "lung cancer prediction", "auc", "nsclc", "lung cancer cases", "ci", "Internal validation", "model"], "article_id"=>1412165, "categories"=>["Biological Sciences"], "users"=>["Magdalena B. Wozniak", "Ghislaine Scelo", "David C. Muller", "Anush Mukeria", "David Zaridze", "Paul Brennan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125026.g002", "stats"=>{"downloads"=>4, "page_views"=>45, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_the_performance_of_the_24_miRNA_classifier_left_and_a_classifier_including_the_24_miRNA_panel_age_at_recruitment_sex_and_smoking_status_right_assessed_using_area_under_Receiver_Operating_Characteristics_ROC_curves_AUCs_in_the_IARC_case_cont/1412165", "title"=>"Evaluation of the performance of the 24-miRNA classifier (left) and a classifier including the 24-miRNA panel, age at recruitment, sex and smoking status (right) assessed using area under Receiver Operating Characteristics (ROC) curves (AUCs) in the IARC case-control study (2006–2012).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-12 03:28:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/2064598"], "description"=>"<p>MiRNA detected in plasma samples of 100 patients with lung cancer vs 100 controls on TaqMan microRNA CARD A and CARD B, respectively in the IARC case-control study (2006–2012).</p>", "links"=>[], "tags"=>["24 microRNAs", "iiia", "Taqman MicroRNA Arrays", "panel", "lung cancer prediction", "auc", "nsclc", "lung cancer cases", "ci", "Internal validation", "model"], "article_id"=>1412164, "categories"=>["Biological Sciences"], "users"=>["Magdalena B. Wozniak", "Ghislaine Scelo", "David C. Muller", "Anush Mukeria", "David Zaridze", "Paul Brennan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0125026.g001", "stats"=>{"downloads"=>2, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MiRNA_detected_in_plasma_samples_of_100_patients_with_lung_cancer_vs_100_controls_on_TaqMan_microRNA_CARD_A_and_CARD_B_respectively_in_the_IARC_case_control_study_2006_8211_2012_/1412164", "title"=>"MiRNA detected in plasma samples of 100 patients with lung cancer vs 100 controls on TaqMan microRNA CARD A and CARD B, respectively in the IARC case-control study (2006–2012).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-12 03:28:33"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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Time
2019-04-06 02:41:28 UTC
Target URL
http://counter-101.soma.plos.org/api/v1.0/stats/doi/10.1371%2Fjournal.pone.0125026
Trace

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/app/jobs/source_job.rb:35:in `perform'