J Cancer Prev.
2016 Sep;21(3):187-193. 10.15430/JCP.2016.21.3.187.
Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer
- Affiliations
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- 1Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
- 2BioInfra, Inc., Seoul, Korea. cwkim@snu.ac.kr
- 3Department of Pathology, Korea Regional Bank, Keimyung University School of Medicine, Daegu, Korea.
- 4Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.
- 5Department of Statistics, College of Natural Science, Seoul National University, Seoul, Korea.
- KMID: 2367577
- DOI: http://doi.org/10.15430/JCP.2016.21.3.187
Abstract
- BACKGROUND
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.
METHODS
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.
RESULTS
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.
CONCLUSIONS
Combining multiple tumor and systemic inflammatory markers proved to be a valid strategy in the diagnosis of lung cancer.
Keyword
MeSH Terms
-
Apolipoprotein A-II
Area Under Curve
Biomarkers
Biomarkers, Tumor
Carcinoembryonic Antigen
Chemokine CCL5
Dataset
Diagnosis
Epididymis
Humans
Lung Neoplasms*
Lung*
Male
Prealbumin
Sensitivity and Specificity
Vascular Cell Adhesion Molecule-1
Apolipoprotein A-II
Biomarkers
Biomarkers, Tumor
Carcinoembryonic Antigen
Chemokine CCL5
Prealbumin
Vascular Cell Adhesion Molecule-1
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