Albini Adriana, Bruno Antonino, Bassani Barbara, D'Ambrosio Gioacchino, Pelosi Giuseppe, Consonni Paolo, Castellani Laura, Conti Matteo, Cristoni Simone, Noonan Douglas M
IRCCS MultiMedica, Milan, Italy.
Department of Medicine and Surgery, University Milano-Bicocca, Milan, Italy.
Front Endocrinol (Lausanne). 2018 Apr 5;9:110. doi: 10.3389/fendo.2018.00110. eCollection 2018.
Serum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples. Steroids have complex biological pathways and stoichiometry and it is important to evaluate their quantitative ratio. MS applications to patient hormone profiling could lead to a diagnostic approach.
Here, we employed the Surface Activated Chemical Ionization-Electrospray-NIST (SANIST) developed in our laboratories, to obtain quantitative serum steroid ratio relationship profiles with a machine learning Bayesian model to discriminate patients with PCa. The approach is focused on steroid relationship profiles and disease association.
A pilot study on patients affected by PCa, benign prostate hypertrophy (BPH), and control subjects [prostate-specific antigen (PSA) lower than 2.5 ng/mL] was done in order to investigate the classification performance of the SANIST platform. The steroid profiles of 71 serum samples (31 controls, 20 patients with PCa and 20 subjects with benign prostate hyperplasia) were evaluated. The levels of 10 steroids were quantitated on the SANIST platform: Aldosterone, Corticosterone, Cortisol, 11-deoxycortisol, Androstenedione, Testosterone, dehydroepiandrosterone, dehydroepiandrosterone sulfate (DHEAS), 17-OH-Progesterone and Progesterone. We performed both traditional and a machine learning analysis.
We show that the machine learning approach based on the steroid relationships developed here was much more accurate than the PSA, DHEAS, and direct absolute value match method in separating the PCa, BPH and control subjects, increasing the sensitivity to 90% and specificity to 84%. This technology, if applied in the future to a larger number of samples will be able to detect the individual enzymatic disequilibrium associated with the steroid ratio and correlate it with the disease. This learning machine approach could be valid in a personalized medicine setting.
血清类固醇是前列腺癌（PCa）中发生改变的关键分子。质谱（MS）是目前用于分析各种生物样品中类固醇的首选技术。类固醇具有复杂的生物途径和化学计量关系，评估它们的定量比率很重要。MS应用于患者激素谱分析可能会带来一种诊断方法。
在此，我们采用了我们实验室开发的表面活化化学电离 - 电喷雾 - NIST（SANIST），通过机器学习贝叶斯模型获得定量血清类固醇比率关系图谱，以区分PCa患者。该方法侧重于类固醇关系图谱和疾病关联。
为了研究SANIST平台的分类性能，对受PCa、良性前列腺增生（BPH）影响的患者以及对照受试者[前列腺特异性抗原（PSA）低于2.5 ng/mL]进行了一项试点研究。评估了71份血清样本（31名对照、20名PCa患者和20名良性前列腺增生受试者）的类固醇谱。在SANIST平台上对10种类固醇的水平进行了定量：醛固酮、皮质酮、皮质醇、11 - 脱氧皮质醇、雄烯二酮、睾酮、脱氢表雄酮、硫酸脱氢表雄酮（DHEAS）、17 - 羟孕酮和孕酮。我们进行了传统分析和机器学习分析。
我们表明，基于此处开发的类固醇关系的机器学习方法在区分PCa、BPH和对照受试者方面比PSA、DHEAS和直接绝对值匹配方法准确得多，将灵敏度提高到90%且特异性提高到84%。如果将来将该技术应用于更多样本，将能够检测与类固醇比率相关的个体酶失衡并将其与疾病相关联。这种机器学习方法在个性化医疗环境中可能是有效的。
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