Analysis and Validation of Characteristic Genes in RNA Sequencing Datasets from Heart Failure Patients Based on Multiple Algorithms

Yuxuan Li¹Ying Bai²Wujiao Wang¹Peng Li¹Dong Li³Sinai Li⁴Jialin Jin^1*Qian Lin^1*

• ¹Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, Beijing Municipality, China
• ²Peking Union Medical College Hospital (CAMS), Beijing, Beijing Municipality, China
• ³Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, Beijing, China
• ⁴Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, Beijing Municipality, China

Background Patients with heart failure (HF) have a poor prognosis and continue to pose a global threat to human health. Consequently, it is crucial to employ bioinformatic approaches to analyze functional alterations within the transcriptome. This analysis should be conducted in conjunction with transcriptome sequencing data from a large sample of clinical myocardial tissue, in order to identify the core pathogenic mechanisms in heart failure myocardial tissue. Method Transcriptome data from HF patient myocardial biopsies underwent Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs). These DEGs were intersected with key genes identified via Weighted Gene Co-expression Network Analysis (WGCNA) in HF. Functional enrichment analysis was performed on the DEGs. Selected key genes were experimentally validated using RT-qPCR in hypertrophic cardiomyocyte models. Single-cell data dimensionality reduction, clustering, and visualization were achieved using Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). Cell types were annotated with SingleR and CellMarker, and single-cell functional enrichment was performed using the "irGSEA" R package. Results RRA of transcriptome data from five studies identified 102 DEGs. Functional enrichment analyses (GO, KEGG, GSEA) revealed associated functional alterations. WGCNA highlighted a key module enriched for energy metabolism-related genes, with the mitochondrial matrix and inner membrane identified as their primary subcellular locations. Integrating RRA-derived DEGs with WGCNA key module genes yielded 14 crucial genes, validated experimentally in a hypertrophic cardiomyocyte model. Analysis of single-cell RNA-seq data identified cold shock domain containing C2 (CSDC2) and Single-pass membrane and coiled-coil domain-containing protein 4 (SMCO4) as cardiomyocyte-specific genes within this set. Subpopulations of cardiomyocytes with high or low expression of SMCO4 and CSDC2 showed strong associations with alterations in fatty acid metabolism, adipogenesis, and oxidative phosphorylation pathways. Conclusion Integrated transcriptomic analysis identified 12 key genes linked to HF, which were validated in a hypertrophy model. Single-cell data showed SMCO4 and CSDC2 are specifically expressed in cardiomyocytes and regulate fatty acid metabolism. This suggests SMCO4 and CSDC2 contribute to HF by altering fatty acid metabolism in heart cells, revealing new disease mechanisms.