AUTHOR=Tu Dingyuan , Ma Chaoqun , Zeng ZhenYu , Xu Qiang , Guo Zhifu , Song Xiaowei , Zhao Xianxian 

TITLE=Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.916429

DOI=10.3389/fcvm.2022.916429

ISSN=2297-055X

ABSTRACT=Backgrounds：Heart failure (HF) is the end stage of various cardiovascular diseases with 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 4 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 robust Rank Aggregation (RRA) method. Then hub genes were recognized through STRING database and Cytoscape software with CytoHubba plug-in. Further, the reliable hub genes were validated by the GEO microarray datasets and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) using heart tissues from HF patients and nonfailing donors (NFDs). In addition, R package “clusterProfiler” and “GSVA” were utilized for enrichment analysis. Moreover, transcription factor (TF)-DEG regulatory network was constructed by Cytoscape and verified in microarray dataset.
Results: 201 robust DEGs were identified between HF patients and NFDs. STRING and Cytoscape analysis recognized 6 hub genes, among which ASPN, COL1A1 and FMOD 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, TF-DEG regulatory network was constructed and 13 significant TF-DEG pairs were finally identified.
Conclusions: Our study integrated different RNA-seq datasets using RUVseq and RRA method and identified ASPN, COL1A1 and FMOD as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.