Limits...
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

Receiver operating curves for FRS and for addition of biomarker expressions in discriminating CAD vs. controls. The improvement of AUC curve suggests that addition of SELDI-TOF-based feature selection biomarkers may add value in CAD risk stratification
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3814567&req=5

Figure 2: Receiver operating curves for FRS and for addition of biomarker expressions in discriminating CAD vs. controls. The improvement of AUC curve suggests that addition of SELDI-TOF-based feature selection biomarkers may add value in CAD risk stratification

Mentions: As we know that FRS is used widely for risk prediction; however we also know that the use of FRS is limited for Asian Indians. Therefore, we considered the nine peaks identified by SELDI-TOF-MS as potential biomarkers along with the FRS model for risk stratification. FRS alone gave an AUC of 0.888, which improved to 0.963 on addition of the nine potential biomarkers [Figure 2]. These nine biomarkers represent seven different pathways, stress and stress/immunity (m/z s 22,859: HSP27, 5896: Leukocyte-specific transcript-1), coagulation (m/z s 8922: Plasminogen precursor activating peptide, 9284: Vitronectin-10, 8600: Pallidin gene isoform-2), infection and inflammation (m/z 19,251: Interferon α-2), mitochondrial damage (m/z 14,660: Farataxin chain-3), calcium binding (m/z 9481: Calmodulin-like protein-4 isoform-3), and cell cycle (m/z 14,720: Cyclin-dependent kinase-4 inhibitor-B). Modulation of these biomarkers results in the change in the functional implications of the pathways, which may result in the disease.


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)

Receiver operating curves for FRS and for addition of biomarker expressions in discriminating CAD vs. controls. The improvement of AUC curve suggests that addition of SELDI-TOF-based feature selection biomarkers may add value in CAD risk stratification
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Receiver operating curves for FRS and for addition of biomarker expressions in discriminating CAD vs. controls. The improvement of AUC curve suggests that addition of SELDI-TOF-based feature selection biomarkers may add value in CAD risk stratification
Mentions: As we know that FRS is used widely for risk prediction; however we also know that the use of FRS is limited for Asian Indians. Therefore, we considered the nine peaks identified by SELDI-TOF-MS as potential biomarkers along with the FRS model for risk stratification. FRS alone gave an AUC of 0.888, which improved to 0.963 on addition of the nine potential biomarkers [Figure 2]. These nine biomarkers represent seven different pathways, stress and stress/immunity (m/z s 22,859: HSP27, 5896: Leukocyte-specific transcript-1), coagulation (m/z s 8922: Plasminogen precursor activating peptide, 9284: Vitronectin-10, 8600: Pallidin gene isoform-2), infection and inflammation (m/z 19,251: Interferon α-2), mitochondrial damage (m/z 14,660: Farataxin chain-3), calcium binding (m/z 9481: Calmodulin-like protein-4 isoform-3), and cell cycle (m/z 14,720: Cyclin-dependent kinase-4 inhibitor-B). Modulation of these biomarkers results in the change in the functional implications of the pathways, which may result in the disease.

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