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Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure
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 progr...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333416/ https://www.ncbi.nlm.nih.gov/pubmed/32620106 http://dx.doi.org/10.1186/s12920-020-00750-9 |
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author | Zhou, Jiamin Zhang, Wei Wei, Chunying Zhang, Zhiliang Yi, Dasong Peng, Xiaoping Peng, Jingtian Yin, Ran Zheng, Zeqi Qi, Hongmei Wei, Yunfeng Wen, Tong |
author_facet | Zhou, Jiamin Zhang, Wei Wei, Chunying Zhang, Zhiliang Yi, Dasong Peng, Xiaoping Peng, Jingtian Yin, Ran Zheng, Zeqi Qi, Hongmei Wei, Yunfeng Wen, Tong |
author_sort | Zhou, Jiamin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7333416 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73334162020-07-06 Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure Zhou, Jiamin Zhang, Wei Wei, Chunying Zhang, Zhiliang Yi, Dasong Peng, Xiaoping Peng, Jingtian Yin, Ran Zheng, Zeqi Qi, Hongmei Wei, Yunfeng Wen, Tong BMC Med Genomics Research Article 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. BioMed Central 2020-07-03 /pmc/articles/PMC7333416/ /pubmed/32620106 http://dx.doi.org/10.1186/s12920-020-00750-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Zhou, Jiamin Zhang, Wei Wei, Chunying Zhang, Zhiliang Yi, Dasong Peng, Xiaoping Peng, Jingtian Yin, Ran Zheng, Zeqi Qi, Hongmei Wei, Yunfeng Wen, Tong Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure |
title | Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure |
title_full | Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure |
title_fullStr | Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure |
title_full_unstemmed | Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure |
title_short | Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure |
title_sort | weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333416/ https://www.ncbi.nlm.nih.gov/pubmed/32620106 http://dx.doi.org/10.1186/s12920-020-00750-9 |
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