Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
CONTEXT: Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2-11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values. OBJECTIVE: Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy. DESIGN: Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19-84 yr) and 45 ACC patients (20-80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26-201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids. RESULTS: Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers. CONCLUSIONS: Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.
Date Issued
2011-12-01
Date Acceptance
2011-08-24
Citation
Journal of Clinical Endocrinology and Metabolism (JCEM), 2011, 96 (12), pp.3775-3784
ISSN
0021-972X
Publisher
Oxford University Press
Start Page
3775
End Page
3784
Journal / Book Title
Journal of Clinical Endocrinology and Metabolism (JCEM)
Volume
96
Issue
12
Copyright Statement
Copyright © 2011 by The Endocrine Society This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/us/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/21917861
PII: jc.2011-1565
Subjects
ADRENOCORTICAL TUMORS
CANCER
CLASSIFICATION
COMPUTED-TOMOGRAPHY
DIAGNOSIS
Endocrinology & Metabolism
FOLLOW-UP
INCIDENTALOMAS
Life Sciences & Biomedicine
Science & Technology
SUBCLINICAL CUSHINGS-SYNDROME
TANDEM MASS-SPECTROMETRY
TIME
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2011-09-14