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

Vangala RK, Ravindran V, Kamath K, Rao VS, Sridhara H - Adv Biomed Res (2013)

Bottom Line: 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.The functional associations of the identified novel biomarkers suggest that they play an important role in the development of disease.

View Article: PubMed Central - PubMed

Affiliation: 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.

ABSTRACT

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.

Materials and 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.

Conclusion: 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.

No MeSH data available.


Related in: MedlinePlus

Representative spectrum report of average m/z 22,859 in CAD and control samples
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Figure 1: Representative spectrum report of average m/z 22,859 in CAD and control samples

Mentions: The spectra of 112 subjects with CAD and 140 controls were analyzed. Fifty-six CAD samples and 70 control samples were used as test set and same number of samples in the as validation set for blind test. A total of 67 m/z clusters were obtained of which 36 were significantly (P < 0.05) differentially expressed. The specific proteins for each m/z were listed in Supplementary Table 1 after the SWISSPROT database search. We obtained nine peaks that could discriminate the cases and controls in the SVM model. The descriptive statistics of these nine peaks are shown in Table 2. Biomarkers with m/z 22,859 [Figure 1], 9284, 14,660, 9481, and 14,720 were highly expressed in CAD-affected subjects, and m/z 5896, 8922, 8600, and 19,251 were highly expressed in controls [Table 2].


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

Vangala RK, Ravindran V, Kamath K, Rao VS, Sridhara H - Adv Biomed Res (2013)

Representative spectrum report of average m/z 22,859 in CAD and control samples
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814567&req=5

Figure 1: Representative spectrum report of average m/z 22,859 in CAD and control samples
Mentions: The spectra of 112 subjects with CAD and 140 controls were analyzed. Fifty-six CAD samples and 70 control samples were used as test set and same number of samples in the as validation set for blind test. A total of 67 m/z clusters were obtained of which 36 were significantly (P < 0.05) differentially expressed. The specific proteins for each m/z were listed in Supplementary Table 1 after the SWISSPROT database search. We obtained nine peaks that could discriminate the cases and controls in the SVM model. The descriptive statistics of these nine peaks are shown in Table 2. Biomarkers with m/z 22,859 [Figure 1], 9284, 14,660, 9481, and 14,720 were highly expressed in CAD-affected subjects, and m/z 5896, 8922, 8600, and 19,251 were highly expressed in controls [Table 2].

Bottom Line: 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.The functional associations of the identified novel biomarkers suggest that they play an important role in the development of disease.

View Article: PubMed Central - PubMed

Affiliation: 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.

ABSTRACT

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.

Materials and 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.

Conclusion: 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.

No MeSH data available.


Related in: MedlinePlus