Albini Adriana, Briga Daniela, Conti Matteo, Bruno Antonino, Farioli Daniela, Canali Sara, Sogno Ilaria, D'Ambrosio Gioacchino, Consonni Paolo, Noonan Douglas M
IRCCS - Arcispedale Santa Maria Nuova in Reggio Emilia, Italy.
Scientific and Technological Pole, IRCCS MultiMedica, Milan, Italy.
Rapid Commun Mass Spectrom. 2015 Oct 15;29(19):1703-10. doi: 10.1002/rcm.7270.
Surface-Activated Chemical Ionization/Electrospray Ionization mass spectrometry (SACI/ESI-MS) is a technique with high sensitivity and low noise that allows accurate biomarker discovery studies. We developed a dedicated SACI/ESI software, named SANIST, for both biomarker fingerprint data acquisition and as a diagnostic tool, using prostate cancer (PCa) as the disease of interest.
Liquid chromatography (LC)/SACI/ESI-MS technology was employed to detect a potential biomarker panel for PCa disease prediction. Serum from patients with histologically confirmed or negative prostate biopsies for PCa was employed. The biomarker data (m/z or Thompson value, retention time and extraction mass chromatogram peak area) were stored in an ascii database. SANIST software allowed identification of potential biomarkers. A Bayesian scoring algorithm developed in house allowed sample separation based on comparison with samples in the database.
Biomarker candidates from the carnitine family were detected at significantly lower levels in patients showing histologically confirmed PCa. Using these biomarkers, the SANIST scoring algorithm allowed separation of patients with PCa from biopsy negative subjects with high accuracy and sensitivity.
SANIST was able to rapidly identify and perform a preliminary evaluation of the potential diagnostic efficiency of potential biomarkers for PCa.
表面活化化学电离/电喷雾电离质谱法(SACI/ESI-MS)是一种具有高灵敏度和低噪音的技术,可用于准确的生物标志物发现研究。我们开发了一款名为SANIST的专用SACI/ESI软件,用于生物标志物指纹数据采集,并作为一种诊断工具,以前列腺癌(PCa)作为研究的疾病。
采用液相色谱(LC)/SACI/ESI-MS技术检测用于预测PCa疾病的潜在生物标志物组。使用经组织学确诊或前列腺活检为PCa阴性的患者的血清。生物标志物数据(m/z或汤普森值、保留时间和提取质量色谱图峰面积)存储在一个ASCII数据库中。SANIST软件可识别潜在的生物标志物。内部开发的贝叶斯评分算法可根据与数据库中样本的比较对样本进行分类。
在经组织学确诊为PCa的患者中,肉碱家族的潜在生物标志物水平显著降低。使用这些生物标志物,SANIST评分算法能够以高准确性和灵敏度将PCa患者与活检阴性的受试者区分开来。
SANIST能够快速识别并对PCa潜在生物标志物的潜在诊断效率进行初步评估。