Vangala Rajani Kanth, Ravindran Vandana, Kamath Karthik, Rao Veena S, Sridhara Hebbagodi
Department of Tata Proteomics and Coagulation, Thrombosis Research Institute, Narayana Hrudayalaya Hospital, Bangalore, Karnataka, India ; Elizabeth and Emmanuel Kaye Bioinformatics and Biostatistics Department, Thrombosis Research Institute, Narayana Hrudayalaya Hospital, Bangalore, Karnataka, India.
Adv Biomed Res. 2013 Jul 30;2:59. doi: 10.4103/2277-9175.115805. eCollection 2013.
Multi-marker approaches for risk prediction in coronary artery disease (CAD) have been inconsistent due to biased selection of specific know biomarkers. We have assessed the global proteome of CAD-affected and unaffected subjects, and developed a pathway network model for elucidating the mechanism and risk prediction for CAD.
A total of 252 samples (112 CAD-affected without family history and 140 true controls) were analyzed by Surface-Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) by using CM10 cationic chips and bioinformatics tools.
Out of 36 significant peaks in SELDI-TOF MS, nine peaks could do better discrimination of CAD subjects and controls (area under the curve (AUC) of 0.963) based on the Support Vector Machine (SVM) feature selection method. Of the nine peaks used in the model for discrimination of CAD-affected and unaffected, the m/z corresponding to 22,859 was identified as stress-related protein HSP27 and was shown to be highly associated with CAD (odds ratio of 3.47). The 36 biomarker peaks were identified and a network profile was constructed showing the functional association between different pathways in CAD.
Based on our data, proteome profiling with SELDI-TOF MS and SVM feature selection methods can be used for novel network biomarker discovery and risk stratification in CAD. The functional associations of the identified novel biomarkers suggest that they play an important role in the development of disease.
由于特定已知生物标志物的选择存在偏差，用于冠状动脉疾病（CAD）风险预测的多标志物方法一直存在不一致性。我们评估了CAD患者和未受影响受试者的整体蛋白质组，并开发了一种通路网络模型来阐明CAD的发病机制和风险预测。
使用CM10阳离子芯片和生物信息学工具，通过表面增强激光解吸/电离飞行时间质谱（SELDI-TOF-MS）对总共252个样本（112例无家族病史的CAD患者和140例真正的对照）进行分析。
在SELDI-TOF MS的36个显著峰中，基于支持向量机（SVM）特征选择方法，9个峰能够更好地区分CAD患者和对照（曲线下面积（AUC）为0.963）。在用于区分CAD患者和未受影响者的模型中使用的9个峰中，对应于22,859的m/z被鉴定为应激相关蛋白HSP27，并显示与CAD高度相关（优势比为3.47）。鉴定出36个生物标志物峰，并构建了一个网络图谱，显示了CAD中不同通路之间的功能关联。
基于我们的数据，使用SELDI-TOF MS和SVM特征选择方法进行蛋白质组分析可用于CAD中新的网络生物标志物发现和风险分层。所鉴定的新型生物标志物的功能关联表明它们在疾病发展中起重要作用。
