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Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors

Please always quote using this URN: urn:nbn:de:bvb:20-opus-154682
  • 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 machineContext: 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.show moreshow less

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Metadaten
Author: Wiebke Arlt, Michael Biehl, Angela E. Taylor, Stefanie Hahner, Rossella Libé, Beverly A. Hughes, Petra Schneider, David J. Smith, Han Stiekema, Nils Krone, Emilio Porfiri, Giuseppe Opocher, Jerôme Bertherat, Franco Mantero, Bruno Allolio, Massimo Terzolo, Peter Nightingale, Cedric H. L. Shackleton, Xavier Bertagna, Martin Fassnacht, Paul M. Stewart
URN:urn:nbn:de:bvb:20-opus-154682
Document Type:Journal article
Faculties:Medizinische Fakultät / Medizinische Klinik und Poliklinik I
Language:English
Parent Title (English):The Journal of Clinical Endocrinology & Metabolism
Year of Completion:2011
Volume:96
Issue:12
Source:The Journal of Clinical Endocrinology & Metabolism, 2011, 96(12):3775-3784. DOI: 10.1210/jc.2011-1565
DOI:https://doi.org/10.1210/jc.2011-1565
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 616 Krankheiten
Tag:adrenal cortex hormones; adrenal cortex neoplasms; mass spectrometry; metabolomics; urine
Release Date:2017/11/08
First Page:3775
Last Page:3784
EU-Project number / Contract (GA) number:259735
OpenAIRE:OpenAIRE
Licence (German):License LogoCC BY-NC: Creative-Commons-Lizenz: Namensnennung, Nicht kommerziell