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Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer
by
Muller, David C.
, Zaridze, David
, Scelo, Ghislaine
, Mukeria, Anush
, Wozniak, Magdalena B.
, Brennan, Paul
in
Aged
/ Alcohol
/ Bioinformatics
/ Biomarkers
/ Biomarkers, Tumor - blood
/ Blood
/ Cancer
/ Cancer diagnosis
/ Carcinoma, Non-Small-Cell Lung - blood
/ Carcinoma, Non-Small-Cell Lung - pathology
/ Case-Control Studies
/ Classification
/ Diagnosis
/ Epidemiology
/ Female
/ Gene expression
/ Genetic aspects
/ Genomes
/ Health risk assessment
/ Health risks
/ Humans
/ Lung cancer
/ Lung diseases
/ Lung Neoplasms - blood
/ Lung Neoplasms - pathology
/ Lungs
/ Male
/ Mathematical models
/ Medical diagnosis
/ Medical prognosis
/ Medical research
/ Medical screening
/ MicroRNA
/ MicroRNAs
/ MicroRNAs - blood
/ Middle Aged
/ miRNA
/ Non-small cell lung cancer
/ Non-small cell lung carcinoma
/ Physiological aspects
/ Plasma
/ Ribonucleic acid
/ Risk analysis
/ Risk factors
/ RNA
/ Sampling methods
/ Smoking
/ Studies
2015
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Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer
by
Muller, David C.
, Zaridze, David
, Scelo, Ghislaine
, Mukeria, Anush
, Wozniak, Magdalena B.
, Brennan, Paul
in
Aged
/ Alcohol
/ Bioinformatics
/ Biomarkers
/ Biomarkers, Tumor - blood
/ Blood
/ Cancer
/ Cancer diagnosis
/ Carcinoma, Non-Small-Cell Lung - blood
/ Carcinoma, Non-Small-Cell Lung - pathology
/ Case-Control Studies
/ Classification
/ Diagnosis
/ Epidemiology
/ Female
/ Gene expression
/ Genetic aspects
/ Genomes
/ Health risk assessment
/ Health risks
/ Humans
/ Lung cancer
/ Lung diseases
/ Lung Neoplasms - blood
/ Lung Neoplasms - pathology
/ Lungs
/ Male
/ Mathematical models
/ Medical diagnosis
/ Medical prognosis
/ Medical research
/ Medical screening
/ MicroRNA
/ MicroRNAs
/ MicroRNAs - blood
/ Middle Aged
/ miRNA
/ Non-small cell lung cancer
/ Non-small cell lung carcinoma
/ Physiological aspects
/ Plasma
/ Ribonucleic acid
/ Risk analysis
/ Risk factors
/ RNA
/ Sampling methods
/ Smoking
/ Studies
2015
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Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer
by
Muller, David C.
, Zaridze, David
, Scelo, Ghislaine
, Mukeria, Anush
, Wozniak, Magdalena B.
, Brennan, Paul
in
Aged
/ Alcohol
/ Bioinformatics
/ Biomarkers
/ Biomarkers, Tumor - blood
/ Blood
/ Cancer
/ Cancer diagnosis
/ Carcinoma, Non-Small-Cell Lung - blood
/ Carcinoma, Non-Small-Cell Lung - pathology
/ Case-Control Studies
/ Classification
/ Diagnosis
/ Epidemiology
/ Female
/ Gene expression
/ Genetic aspects
/ Genomes
/ Health risk assessment
/ Health risks
/ Humans
/ Lung cancer
/ Lung diseases
/ Lung Neoplasms - blood
/ Lung Neoplasms - pathology
/ Lungs
/ Male
/ Mathematical models
/ Medical diagnosis
/ Medical prognosis
/ Medical research
/ Medical screening
/ MicroRNA
/ MicroRNAs
/ MicroRNAs - blood
/ Middle Aged
/ miRNA
/ Non-small cell lung cancer
/ Non-small cell lung carcinoma
/ Physiological aspects
/ Plasma
/ Ribonucleic acid
/ Risk analysis
/ Risk factors
/ RNA
/ Sampling methods
/ Smoking
/ Studies
2015
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Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer
Journal Article
Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer
2015
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Overview
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.
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.
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.
Our 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors.
Publisher
Public Library of Science,Public Library of Science (PLoS)
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