Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
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.
Rajani Kanth
RK
Vangala
Vandana
V
Ravindran
Karthik
K
Kamath
Veena S
VS
Rao
Hebbagodi
H
Sridhara
Coronary artery disease; HSP27; Surface-Enhanced Laser Desorption/Ionization; networking biomarkers; risk prediction