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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2013-01-01
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| Series: | Advanced Biomedical Research |
| Subjects: | |
| Online Access: | http://www.advbiores.net/article.asp?issn=2277-9175;year=2013;volume=2;issue=1;spage=59;epage=59;aulast=Vangala |
| _version_ | 1811216899910926336 |
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| author | Rajani Kanth Vangala Vandana Ravindran Karthik Kamath Veena S Rao Hebbagodi Sridhara |
| author_facet | Rajani Kanth Vangala Vandana Ravindran Karthik Kamath Veena S Rao Hebbagodi Sridhara |
| author_sort | Rajani Kanth Vangala |
| collection | DOAJ |
| description | 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. |
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| format | Article |
| id | doaj.art-ebad9bb10b0748cd8c8cd3d4bff2d93f |
| institution | Directory Open Access Journal |
| issn | 2277-9175 2277-9175 |
| language | English |
| last_indexed | 2024-04-12T06:45:21Z |
| publishDate | 2013-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Advanced Biomedical Research |
| spelling | doaj.art-ebad9bb10b0748cd8c8cd3d4bff2d93f2022-12-22T03:43:36ZengWolters Kluwer Medknow PublicationsAdvanced Biomedical Research2277-91752277-91752013-01-0121595910.4103/2277-9175.115805Novel network biomarkers profile based coronary artery disease risk stratification in Asian IndiansRajani Kanth VangalaVandana RavindranKarthik KamathVeena S RaoHebbagodi SridharaBackground: 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.http://www.advbiores.net/article.asp?issn=2277-9175;year=2013;volume=2;issue=1;spage=59;epage=59;aulast=VangalaCoronary artery diseaseHSP27networking biomarkersrisk predictionSurface-Enhanced Laser Desorption/Ionization |
| spellingShingle | Rajani Kanth Vangala Vandana Ravindran Karthik Kamath Veena S Rao Hebbagodi Sridhara Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians Advanced Biomedical Research Coronary artery disease HSP27 networking biomarkers risk prediction Surface-Enhanced Laser Desorption/Ionization |
| title | Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians |
| title_full | Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians |
| title_fullStr | Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians |
| title_full_unstemmed | Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians |
| title_short | Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians |
| title_sort | novel network biomarkers profile based coronary artery disease risk stratification in asian indians |
| topic | Coronary artery disease HSP27 networking biomarkers risk prediction Surface-Enhanced Laser Desorption/Ionization |
| url | http://www.advbiores.net/article.asp?issn=2277-9175;year=2013;volume=2;issue=1;spage=59;epage=59;aulast=Vangala |
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