| Title: |
Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors. |
| Authors: |
Arlt W; Biehl M; Taylor AE; Hahner S; Libé R; Hughes BA; Schneider P; Smith DJ; Stiekema H; Krone N; Porfiri E; Opocher G; Bertherat J; Mantero F; Allolio B; Nightingale P; Shackleton CH; Bertagna X; Fassnacht M; Stewart P.M.; TERZOLO, Massimo |
| Contributors: |
Arlt W; Biehl M; Taylor AE; Hahner S; Libé R; Hughes BA; Schneider P; Smith DJ; Stiekema H; Krone N; Porfiri E; Opocher G; Bertherat J; Mantero F; Allolio B; Terzolo M; Nightingale P; Shackleton CH; Bertagna X; Fassnacht M; Stewart PM. |
| Publication Year: |
2011 |
| Collection: |
Università degli studi di Torino: AperTo (Archivio Istituzionale ad Accesso Aperto) |
| Subject Terms: |
Adrenocortical cancer; mitotane |
| Description: |
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.GMLVQanalysis 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 curve0.97) 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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
STAMPA |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/21917861; info:eu-repo/semantics/altIdentifier/wos/WOS:000298295200052; volume:96; firstpage:3775; lastpage:3784; numberofpages:10; journal:THE JOURNAL OF CLINICAL ENDOCRINOLOGY AND METABOLISM; info:eu-repo/grantAgreement/EC/FP7/259735; http://hdl.handle.net/2318/109793; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-83155177174 |
| DOI: |
10.1210/jc.2011-1565 |
| Availability: |
http://hdl.handle.net/2318/109793; https://doi.org/10.1210/jc.2011-1565 |
| Rights: |
info:eu-repo/semantics/closedAccess |
| Accession Number: |
edsbas.91C0B732 |
| Database: |
BASE |