A three-tiered integrative analysis of transcriptional data reveals the shared pathways related to heart failure from

Zhenhong Jiang1, Ninghong Guo1, Kui Hong1,2

  • 1The Jiangxi Key Laboratory of Molecular Medicine, Nanchang, China.

Insights

This study reveals common molecular underpinnings across diverse causes of heart failure (HF). Identifying frequently dysregulated genes and pathways offers new insights into HF pathogenesis and potential therapeutic targets.

Area of Science:

  • Cardiovascular Biology
  • Systems Biology
  • Genomics

Background:

  • Heart failure (HF) represents the advanced stage of numerous cardiac conditions, arising from various etiological factors.
  • The molecular commonalities underlying HF across different causes remain largely unexplored.

Purpose of the Study:

  • To investigate the shared molecular mechanisms of heart failure (HF) irrespective of its diverse origins.
  • To provide a systems biology perspective on HF by integrating transcriptional and pathway data.

Main Methods:

  • Differential gene expression analysis across 11 HF etiologies to identify common differentially expressed genes.
  • Gene set enrichment analysis to pinpoint frequently dysregulated pathways.
  • Regulatory network integration to map transcriptional factor (TF)-pathway interactions.

Main Results:

  • Identified 111 frequently differentially expressed genes in HF, including known biomarkers (NPPA, NPPB) and novel candidates (SERPINA3, STAT4).
  • Discovered 19 frequently dysregulated pathways, highlighting the roles of immune signaling, extracellular matrix, and metabolism.
  • Established 241 regulatory relationships between 64 TFs and 17 pathways, implicating key TFs in HF development.

Conclusions:

  • HF exhibits molecular commonalities across different etiologies, suggesting shared pathogenic processes.
  • The findings underscore the importance of immune, extracellular matrix, and metabolic pathways in HF.
  • This systems biology approach offers a comprehensive view of HF molecular underpinnings and identifies potential therapeutic targets.

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