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Multiplexed Serum Biomarkers for the Detection of Lung Cancer.

Abstract
Currently, there is no available biomarker for lung cancer diagnosis. Here we recruited 844 lung cancer patients and 620 healthy participants from six hospitals. A total of four serum proteins was identified and subsequently assessed in the training and validation cohorts. The concentrations of four serum proteins were found to be significantly higher in lung cancer patients compared with healthy participants. The area under the curve (AUC) for the 4-biomarker were 0.86 in the training cohort, and 0.87 in the validation cohort. The classification improved to a corrected AUC of 0.90 and 0.89 respectively following addition of sex, age and smoking status. Similar results were observed for early-stage lung cancer. Remarkably, in a blinded test with a suspicious pulmonary nodule, the adjusted prediction model correctly discriminated the patients with 86.96% sensitivity and 98.25% specificity. These results demonstrated the 4-biomarker panel improved lung cancer prediction beyond that of known risk factors. Moreover, the biomarkers were valuable in differentiating benign nodules which will remain indolent from those that are likely to progress and therefore might serve as an adjuvant diagnosis tool for LDCT scanning.
AuthorsShenglin Ma, Wenzhe Wang, Bing Xia, Shirong Zhang, Haining Yuan, Hong Jiang, Wen Meng, Xiaoliang Zheng, Xiaoju Wang
JournalEBioMedicine (EBioMedicine) Vol. 11 Pg. 210-218 (Sep 2016) ISSN: 2352-3964 [Electronic] Netherlands
PMID27575387 (Publication Type: Journal Article, Multicenter Study)
CopyrightCopyright © 2016 Forschungsgesellschaft für Arbeitsphysiologie und Arbeitschutz e.V. Published by Elsevier B.V. All rights reserved.
Chemical References
  • Biomarkers
Topics
  • Adult
  • Aged
  • Biomarkers
  • Case-Control Studies
  • Early Detection of Cancer
  • Female
  • Humans
  • Lung Neoplasms (blood, diagnosis)
  • Male
  • Middle Aged
  • Neoplasm Staging
  • ROC Curve
  • Reproducibility of Results
  • Risk Factors
  • Tomography, X-Ray Computed

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