Table_5_Diagnostic potential of energy metabolism-related genes in heart failure with preserved ejection fraction.xlsx

BackgroundHeart failure with preserved ejection fraction (HFpEF) is associated with changes in cardiac metabolism that affect energy supply in the heart. However, there is limited research on energy metabolism-related genes (EMRGs) in HFpEF.
MethodsThe HFpEF mouse dataset (GSE180065, containing heart tissues from 10 HFpEF and five control samples) was sourced from the Gene Expression Omnibus database. Gene expression profiles in HFpEF and control groups were compared to identify differentially expressed EMRGs (DE-EMRGs), and the diagnostic biomarkers with diagnostic value were screened using machine learning algorithms. Meanwhile, we constructed a biomarker-based nomogram model for its predictive power, and functionality of diagnostic biomarkers were conducted using single-gene gene set enrichment analysis, drug prediction, and regulatory network analysis. Additionally, consensus clustering analysis based on the expression of diagnostic biomarkers was utilized to identify differential HFpEF-related genes (HFpEF-RGs). Immune microenvironment analysis in HFpEF and subtypes were performed for analyzing correlations between immune cells and diagnostic biomarkers as well as HFpEF-RGs. Finally, qRT-PCR analysis on the HFpEF mouse model was used to validate the expression levels of diagnostic biomarkers.
ResultsWe selected 5 biomarkers (Chrna2, Gnb3, Gng7, Ddit4l, and Prss55) that showed excellent diagnostic performance. The nomogram model we constructed demonstrated high predictive power. Single-gene gene set enrichment analysis revealed enrichment in aerobic respiration and energy derivation. Further, various miRNAs and TFs were predicted by Gng7, such as Gng7-mmu-miR-6921-5p, ETS1-Gng7. A lot of potential therapeutic targets were predicted as well. Consensus clustering identified two distinct subtypes of HFpEF. Functional enrichment analysis highlighted the involvement of DEGs-cluster in protein amino acid modification and so on. Additionally, we identified five HFpEF-RGs (Kcnt1, Acot1, Kcnc4, Scn3a, and Gpam). Immune analysis revealed correlations between Macrophage M2, T cell CD4+ Th1 and diagnostic biomarkers, as well as an association between Macrophage and HFpEF-RGs. We further validated the expression trends of the selected biomarkers through experimental validation.
ConclusionOur study identified 5 diagnostic biomarkers and provided insights into the prediction and treatment of HFpEF through drug predictions and network analysis. These findings contribute to a better understanding of HFpEF and may guide future research and therapy development.

背景 射血分数保留型心力衰竭（Heart Failure with Preserved Ejection Fraction, HFpEF）与影响心脏能量供给的心肌代谢改变密切相关，但目前针对HFpEF中能量代谢相关基因（Energy Metabolism-Related Genes, EMRGs）的研究仍较为有限。
方法 本研究从基因表达汇编（Gene Expression Omnibus, GEO）数据库获取HFpEF小鼠数据集（GSE180065），该数据集包含10例HFpEF小鼠心脏组织样本与5例对照样本。通过对比HFpEF组与对照组的基因表达谱，筛选差异表达能量代谢相关基因（Differentially Expressed Energy Metabolism-Related Genes, DE-EMRGs），并采用机器学习算法筛选具有诊断价值的生物标志物。同时，基于筛选得到的生物标志物构建列线图（nomogram）模型以评估其预测效能，并通过单基因基因集富集分析、药物预测及调控网络分析探究诊断生物标志物的功能。此外，基于诊断生物标志物的表达水平开展共识聚类分析，以识别HFpEF相关差异基因（HFpEF-Related Genes, HFpEF-RGs）。对HFpEF模型及亚型开展免疫微环境分析，以解析免疫细胞与诊断生物标志物、HFpEF-RGs之间的相关性。最后，通过HFpEF小鼠模型的qRT-PCR验证诊断生物标志物的表达水平。
结果 本研究筛选得到5个具有优异诊断效能的生物标志物（Chrna2、Gnb3、Gng7、Ddit4l及Prss55）。所构建的列线图模型展现出较高的预测效能。单基因基因集富集分析结果显示，这些生物标志物显著富集于有氧呼吸及能量生成通路。进一步通过Gng7预测得到多种微小RNA（microRNAs, miRNAs）与转录因子（Transcription Factors, TFs），如Gng7-mmu-miR-6921-5p、ETS1-Gng7，同时还预测得到大量潜在治疗靶点。共识聚类分析将HFpEF划分为两个截然不同的亚型。功能富集分析表明，差异表达基因簇（DEGs-cluster）参与蛋白质氨基酸修饰等生物学过程。此外，本研究还识别出5个HFpEF-RGs（Kcnt1、Acot1、Kcnc4、Scn3a及Gpam）。免疫分析结果显示，M2型巨噬细胞、CD4+辅助性T细胞1型（CD4+ Th1）与诊断生物标志物存在相关性，同时巨噬细胞与HFpEF-RGs亦存在关联。最后通过实验验证进一步确认了所选生物标志物的表达趋势。
结论 本研究识别出5个HFpEF诊断生物标志物，并通过药物预测与网络分析为HFpEF的预测及治疗提供了新的研究视角。上述研究结果有助于加深对HFpEF的认知，可为后续相关研究与治疗方案开发提供指导。