Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis
Dingyuan Tu, Chaoqun Ma, Zhenyu Zeng, Qiang Xu, Zhifu Guo, Xiaowei Song, Xianxian Zhao
Abstract
Background: Heart failure (HF) is the end stage of various cardiovascular diseases with a high mortality rate. Novel diagnostic and therapeutic biomarkers for HF are urgently required. Our research aims to identify HF-related hub genes and regulatory networks using bioinformatics and validation assays. Methods: Using four RNA-seq datasets in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of HF using Removal of Unwanted Variation from RNA-seq data (RUVSeq) and the robust rank aggregation (RRA) method. Then, hub genes were recognized using the STRING database and Cytoscape software with cytoHubba plug-in. Furthermore, reliable hub genes were validated by the GEO microarray datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR) using heart tissues from patients with HF and non-failing donors (NFDs). In addition, R packages "clusterProfiler" and "GSVA" were utilized for enrichment analysis. Moreover, the transcription factor (TF)-DEG regulatory network was constructed by Cytoscape and verified in a microarray dataset. Results: were confirmed as reliable hub genes through microarray datasets and qRT-PCR validation. Functional analysis showed that the DEGs and hub genes were enriched in T-cell-mediated immune response and myocardial glucose metabolism, which were closely associated with myocardial fibrosis. In addition, the TF-DEG regulatory network was constructed, and 13 significant TF-DEG pairs were finally identified. Conclusion: as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.