Discovering Novel Genetic Drivers of Chronic Heart Failure Through RNA-Seq Analysis

Overview

Can integrating a dataset uncover heart failure that may be overlooked in single-study analyses? Chronic Heart Failure (CHF) shows widespread remodeling, yet many individual studies are constrained by small sets/batches or dataset-specific bias. Instead, a bioinformatic workflow was used to combine the GSE116250 datasets from the NCBI GEO database, generating an transcriptomic profile of CHF spanning inflammatory processes, metabolic regulation, and cellular stress signaling pathways. Following raw count normalization via DESeq2, a differential expression analysis was done to identify genes with a reproducible log2 fold change exceeding or equal to 1. To move beyond individual gene lists and identify pathway disruptions, Weighted Gene Co-expression Network Analysis (WGCNA) was used to cluster co-regulated genes into functional modules. The Turquoise Module was found as the primary driver of disease, enriched in pathways associated with mitochondrial dysfunction and oxidative stress, which are key indicators of the ‘myocardial energy crisis’ of CHF. Through cross-dataset validation, CCDC93, CD36, and NQO1 were identified as the most consistently upregulated genetic drivers. While CD36 and NQO1 show shifts in metabolic regulation and antioxidant defense, the identification of CCDC93 points to a novel, often overlooked signaling in heart failure pathology. By synthesizing data across independent studies, this provides a method for prioritizing therapeutic targets that remain upregulated across the dataset, offering a new perspective of genes for precision cardiology.