The skeletal muscle hypothesis describes a vicious cycle of progressive decline in left ventricular function, coupled with remodeling of skeletal muscle and diminished functional capacity in patients with heart failure (HF). While some underlying processes have been characterized, the regulatory mechanisms and their associations with clinical status and prognosis are still largely unclear. Here, we present a comprehensive network-centric analysis of RNA sequencing data from skeletal muscle biopsies of HF patients, aiming to characterise key processes implicated in disease pathophysiology.
We constructed a scale-free co-expression network using Weighted Gene Co-Expression Network Analysis (WGCNA), and identified 14 distinct gene communities involved in well-established biological processes within human skeletal muscle. To ensure the validity of the constructed network, we recalculated all network edges and confirmed their conservation of co-expression patterns in two independent cohorts. This validation process utilized Pearson correlation ≥0.3, and the module-preservation statistic-feature (Z-score ≥3) from the WGCNA framework. Confirmed network communities were then reduced to a single per community measure, eigengene, using principal component analysis and associated with clinically derived variables of HF patients. The network communities were also analyzed for prognostic relevance and enrichment of genes associated with clinical conditions of importance in HF such as, increased physical activity, bed rest, cancer cachexia, and cellular senescence.
Based on the association with clinical features and prognosis, extracellular matrix remodeling, mitochondrial beta-oxidation, and p53 signaling communities were identified as key processes. The former two communities were highly enriched with genes regulated by physical (in)activity, and weak association with prognosis. Community related to p53 signaling, with CDKN1A as a key regulator, was increased in HF patients compared to age-matched controls and associated with worse prognosis. The current work differentiates previously proposed factors underlying heart failure-induced skeletal muscle dysfunction, emphasizes the p53 signaling community and importance of biological age in this process.