Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors.
Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors.
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DOI：
10.1210/jc.2011-1565
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发表时间：
2011-12
期刊：
影响因子：
--
通讯作者：
Stewart PM
中科院分区：
文献类型：
--
作者：
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
肾上腺肿瘤在普通人群中的患病率约为2%。肾上腺皮质癌(ACC)很少见，但占偶然发现的肾上腺肿块的2-11%。在肾上腺偶发瘤患者中，鉴别ACC和肾上腺皮质腺瘤(ACA)是一项诊断挑战，肿瘤大小、影像，甚至组织学都不能提供令人满意的预测价值。在这里，我们开发了一种新的类固醇代谢方法，即基于质谱学的类固醇图谱，然后进行机器学习分析，并检验其对肾上腺恶性肿瘤的诊断价值。用气相色谱/质谱法对102例ACA患者(年龄19~84岁)和45例ACC患者(20~80岁)的24 h尿样中32种不同的肾上腺来源的类固醇进行了定量。基本诊断是通过ACC的组织学和转移以及临床随访[中位持续时间52(范围26-201)个月]确定的，没有ACA转移的证据。类固醇排泄数据经过广义矩阵学习矢量量化(GMLVQ)，以识别最具区分性的类固醇。类固醇分析显示，在ACC中主要是不成熟的、早期的类固醇生成模式。GMLVQ分析确定了9种类固醇的子集，它们在区分ACA和ACC方面表现最好。GMLVQ结果的受试者操作特征分析显示，仅使用9个最具区分性的标记物时，使用所有32种类固醇激素的灵敏度=特异度=90%(曲线下面积=0.97)，灵敏度=特异度=88%(曲线下面积=0.96)。尿类固醇代谢组学是鉴别肾上腺肿瘤良恶性的一种新的、高度敏感和特异的生物标志物，对肾上腺偶发瘤患者的诊断具有明显的应用前景。
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. 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. 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. 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. 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.
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通讯作者：
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