Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer

Despite major advances in lung cancer treatment, early detection remains the most promising way of improving outcomes. To detect lung cancer in earlier stages, many serum biomarkers have been tested. Unfortunately, no single biomarker can reliably detect lung cancer. We combined a set of 2 tumor mar...

Full description

Saved in:
Bibliographic Details
Published in:Journal of cancer prevention 2016, 21(3), , pp.187-193
Main Authors: Yoon, Ho Il, Kwon, Oh-Ran, Kang, Kyung Nam, Shin, Yong Sung, Shin, Ho Sang, Yeon, Eun Hee, Kwon, Keon Young, Hwang, Ilseon, Jeon, Yoon Kyung, Kim, Yongdai, Kim, Chul Woo
Format: Article
Language:English
Subjects:
Original
예방의학
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Despite major advances in lung cancer treatment, early detection remains the most promising way of improving outcomes. To detect lung cancer in earlier stages, many serum biomarkers have been tested. Unfortunately, no single biomarker can reliably detect lung cancer. We combined a set of 2 tumor markers and 4 inflammatory or metabolic markers and tried to validate the diagnostic performance in lung cancer. We collected serum samples from 355 lung cancer patients and 590 control subjects and divided them into training and validation datasets. After measuring serum levels of 6 biomarkers (human epididymis secretory protein 4 [HE4], carcinoembryonic antigen [CEA], regulated on activation, normal T cell expressed and secreted [RANTES], apolipoprotein A2 [ApoA2], transthyretin [TTR], and secretory vascular cell adhesion molecule-1 [sVCAM-1]), we tested various sets of biomarkers for their diagnostic performance in lung cancer. In a training dataset, the area under the curve (AUC) values were 0.821 for HE4, 0.753 for CEA, 0.858 for RANTES, 0.867 for ApoA2, 0.830 for TTR, and 0.552 for sVCAM-1. A model using all 6 biomarkers and age yielded an AUC value of 0.986 and sensitivity of 93.2% (cutoff at specificity 94%). Applying this model to the validation dataset showed similar results. The AUC value of the model was 0.988, with sensitivity of 93.33% and specificity of 92.00% at the same cutoff point used in the validation dataset. Analyses by stages and histologic subtypes all yielded similar results. Combining multiple tumor and systemic inflammatory markers proved to be a valid strategy in the diagnosis of lung cancer.
ISSN:2288-3649
2288-3657
DOI:10.15430/JCP.2016.21.3.187