ArticlePloS one2013
Integrative bioinformatics analysis of genomic and proteomic approaches to understand the transcriptional regulatory program in coronary artery disease pathways.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
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- The KLF7/PFKL/ACADL axis modulates cardiac metabolic remodelling during cardiac hypertrophy in male mice.Nature communications · 2023Article
- miR-132-3p and KLF7 as novel regulators of aortic stiffening-associated EndMT in type 2 diabetes mellitus.Diabetology & metabolic syndrome · 2023Article
- KLF7 promotes preadipocyte proliferation via activation of the Akt signaling pathway byActa biochimica et biophysica Sinica · 2022Article
- Melatonin Alleviates Oxidative Stress Induced by HAntioxidants (Basel, Switzerland) · 2022Article
- GATA Binding Protein 3 Is a Direct Target of Kruppel-Like Transcription Factor 7 and Inhibits Chicken Adipogenesis.Frontiers in physiology · 2020Article
- Proteomics in India: the clinical aspect.Clinical proteomics · 2016Review
- Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.PloS one · 2014Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.
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Registered trials
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