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Molecular prediction for atherogenic risks across different cell types of leukocytes

BACKGROUND: Diagnosing subclinical atherosclerosis is often difficult since patients are asymptomatic. In order to alleviate this limitation, we have developed a molecular prediction technique for predicting patients with atherogenic risks using multi-gene expression biomarkers on leukocytes. METHOD...

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Detalles Bibliográficos
Autores principales: Cheng, Feng, Keeley, Ellen C, Lee, Jae K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3271975/
https://www.ncbi.nlm.nih.gov/pubmed/22244445
http://dx.doi.org/10.1186/1755-8794-5-2
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author Cheng, Feng
Keeley, Ellen C
Lee, Jae K
author_facet Cheng, Feng
Keeley, Ellen C
Lee, Jae K
author_sort Cheng, Feng
collection PubMed
description BACKGROUND: Diagnosing subclinical atherosclerosis is often difficult since patients are asymptomatic. In order to alleviate this limitation, we have developed a molecular prediction technique for predicting patients with atherogenic risks using multi-gene expression biomarkers on leukocytes. METHODS: We first discovered 356 expression biomarkers which showed significant differential expression between genome-wide microarray data of monocytes from patients with familial hyperlipidemia and increased risk of atherosclerosis compared to normal controls. These biomarkers were further triaged with 56 biomarkers known to be directly related to atherogenic risks. We also applied a COXEN algorithm to identify concordantly expressed biomarkers between monocytes and each of three different cell types of leukocytes. We then developed a multi-gene predictor using all or three subsets of these 56 biomarkers on the monocyte patient data. These predictors were then applied to multiple independent patient sets from three cell types of leukocytes (macrophages, circulating T cells, or whole white blood cells) to predict patients with atherogenic risks. RESULTS: When the 56 predictor was applied to the three patient sets from different cell types of leukocytes, all significantly stratified patients with atherogenic risks from healthy people in these independent cohorts. Concordantly expressed biomarkers identified by the COXEN algorithm provided slightly better prediction results. CONCLUSION: These results demonstrated the potential of molecular prediction of atherogenic risks across different cell types of leukocytes.
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spelling pubmed-32719752012-02-06 Molecular prediction for atherogenic risks across different cell types of leukocytes Cheng, Feng Keeley, Ellen C Lee, Jae K BMC Med Genomics Research Article BACKGROUND: Diagnosing subclinical atherosclerosis is often difficult since patients are asymptomatic. In order to alleviate this limitation, we have developed a molecular prediction technique for predicting patients with atherogenic risks using multi-gene expression biomarkers on leukocytes. METHODS: We first discovered 356 expression biomarkers which showed significant differential expression between genome-wide microarray data of monocytes from patients with familial hyperlipidemia and increased risk of atherosclerosis compared to normal controls. These biomarkers were further triaged with 56 biomarkers known to be directly related to atherogenic risks. We also applied a COXEN algorithm to identify concordantly expressed biomarkers between monocytes and each of three different cell types of leukocytes. We then developed a multi-gene predictor using all or three subsets of these 56 biomarkers on the monocyte patient data. These predictors were then applied to multiple independent patient sets from three cell types of leukocytes (macrophages, circulating T cells, or whole white blood cells) to predict patients with atherogenic risks. RESULTS: When the 56 predictor was applied to the three patient sets from different cell types of leukocytes, all significantly stratified patients with atherogenic risks from healthy people in these independent cohorts. Concordantly expressed biomarkers identified by the COXEN algorithm provided slightly better prediction results. CONCLUSION: These results demonstrated the potential of molecular prediction of atherogenic risks across different cell types of leukocytes. BioMed Central 2012-01-13 /pmc/articles/PMC3271975/ /pubmed/22244445 http://dx.doi.org/10.1186/1755-8794-5-2 Text en Copyright ©2012 Cheng et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cheng, Feng
Keeley, Ellen C
Lee, Jae K
Molecular prediction for atherogenic risks across different cell types of leukocytes
title Molecular prediction for atherogenic risks across different cell types of leukocytes
title_full Molecular prediction for atherogenic risks across different cell types of leukocytes
title_fullStr Molecular prediction for atherogenic risks across different cell types of leukocytes
title_full_unstemmed Molecular prediction for atherogenic risks across different cell types of leukocytes
title_short Molecular prediction for atherogenic risks across different cell types of leukocytes
title_sort molecular prediction for atherogenic risks across different cell types of leukocytes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3271975/
https://www.ncbi.nlm.nih.gov/pubmed/22244445
http://dx.doi.org/10.1186/1755-8794-5-2
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