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Start Over Author "Xiaoping Peng" Journal bmc medical genomics
2 results on '"Xiaoping Peng"'

1. Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

Author

Jiamin Zhou, Wei Zhang, Chunying Wei, Zhiliang Zhang, Dasong Yi, Xiaoping Peng, Jingtian Peng, Ran Yin, Zeqi Zheng, Hongmei Qi, Yunfeng Wei, and Tong Wen

Subjects
Weighted gene co-expression network analysis, Ischemic heart disease, Dilated cardiomyopathy, Heart failure, Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background Left-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis. Methods A total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset. Results A total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrix-associated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix. Conclusion We uncovered a candidate gene signature correlated with HF, ISCH and CMP in the left ventricle, which may help provide better prognosis and therapeutic decisions and in HF, ISCH and CMP patients.

Published
2020
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2. Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

Author

Jingtian Peng, Tong Wen, Chunying Wei, Wei Zhang, Ran Yin, Jiamin Zhou, Xiaoping Peng, Yunfeng Wei, Hongmei Qi, Dasong Yi, Zhiliang Zhang, and Zeqi Zheng

Subjects
Cardiomyopathy, Dilated, 0301 basic medicine, lcsh:Internal medicine, Candidate gene, lcsh:QH426-470, Ischemic heart disease, Dilated cardiomyopathy, Computational biology, 030204 cardiovascular system & hematology, Extracellular matrix structural constituent, 03 medical and health sciences, 0302 clinical medicine, Weighted gene co-expression network analysis, Gene expression, Genetics, Humans, Gene Regulatory Networks, lcsh:RC31-1245, Transcription factor, Gene, Genetics (clinical), Heart Failure, biology, Gene Expression Profiling, Computational Biology, Transforming growth factor beta, Prognosis, Human genetics, lcsh:Genetics, 030104 developmental biology, biology.protein, DNA microarray, Biomarkers, Research Article
Abstract

BackgroundLeft-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis.MethodsA total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset.ResultsA total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrix-associated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix.ConclusionWe uncovered a candidate gene signature correlated with HF, ISCH and CMP in the left ventricle, which may help provide better prognosis and therapeutic decisions and in HF, ISCH and CMP patients.

Published
2020

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