A comprehensive bioinformatics analysis identifies mitophagy biomarkers and potential Molecular mechanisms in hypertensive nephropathy

Mitophagy, the selective removal of damaged mitochondria, plays a critical role in kidney diseases, but its involvement in hypertensive nephropathy (HTN) is not well understood. To address this gap, we investigated mitophagy-related genes in HTN, identifying potential biomarkers for diagnosis and treatment. Transcriptome datasets from the Gene Expression Omnibus database were analyzed, resulting in the identification of seven mitophagy related differentially expressed genes (MR-DEGs), namely PINK1, ULK1, SQSTM1, ATG5, ATG12, MFN2, and UBA52. Further, we explored the correlation between MR-DEGs, immune cells, and inflammatory factors. The identified genes demonstrated a strong correlation with Mast cells, T-cells, TGFβ3, IL13, and CSF3. Machine learning techniques were employed to screen important genes, construct diagnostic models, and evaluate their accuracy. Consensus clustering divided the HTN patients into two mitophagy subgroups, with Subgroup 2 showing higher levels of immune cell infiltration and inflammatory factors. The functions of their proteins primarily involve complement, coagulation, lipids, and vascular smooth muscle contraction. Single-cell RNA sequencing revealed that mitophagy was most significant in proximal tubule cells (PTC) in HTN patients. Pseudotime analysis of PTC confirmed the expression changes observed in the transcriptome. Intercellular communication analysis suggested that mitophagy might regulate PTC's participation in intercellular crosstalk. Notably, specific transcription factors such as HNF4A, PPARA, and STAT3 showed strong correlations with mitophagy-related genes in PTC, indicating their potential role in modulating PTC function and influencing the onset and progression of HTN. This study offers a comprehensive analysis of mitophagy in HTN, enhancing our understanding of the pathogenesis, diagnosis, and treatment of HTN.

线粒体自噬（Mitophagy）作为选择性清除受损线粒体的过程，在肾脏疾病中发挥关键作用，但其在高血压肾病（Hypertensive Nephropathy, HTN）中的参与机制尚未被充分阐明。为填补这一研究空白，本研究针对HTN中的线粒体自噬相关基因展开探究，以期筛选出可用于疾病诊断与治疗的潜在生物标志物。本研究对基因表达综合数据库（Gene Expression Omnibus, GEO）中的转录组数据集进行分析，最终筛选得到7个线粒体自噬相关差异表达基因（mitophagy-related differentially expressed genes, MR-DEGs），分别为PINK1、ULK1、SQSTM1、ATG5、ATG12、MFN2及UBA52。此外，本研究还探究了MR-DEGs与免疫细胞、炎症因子之间的相关性。筛选得到的基因与肥大细胞、T细胞、转化生长因子β3（TGFβ3）、白细胞介素13（IL13）及集落刺激因子3（CSF3）均呈现显著相关性。本研究采用机器学习技术筛选核心基因、构建诊断模型并评估其诊断效能。通过共识聚类分析，本研究将HTN患者划分为两个线粒体自噬亚型，其中亚型2的免疫细胞浸润程度与炎症因子水平均更高。这些基因编码的蛋白质主要参与补体激活、凝血过程、脂质代谢及血管平滑肌收缩等生理病理过程。单细胞RNA测序（single-cell RNA sequencing）分析显示，HTN患者的近端肾小管上皮细胞（proximal tubule cells, PTC）中线粒体自噬活性最为显著。针对PTC的拟时间分析验证了转录组分析中观察到的基因表达变化趋势。细胞间通讯分析结果表明，线粒体自噬可能通过调控PTC参与细胞间的信号串扰。值得注意的是，部分特异性转录因子（transcription factors）如肝细胞核因子4α（HNF4A）、过氧化物酶体增殖物激活受体α（PPARA）及信号转导与转录激活因子3（STAT3）与PTC中的线粒体自噬相关基因呈现显著相关性，提示这些转录因子可能通过调控PTC功能，进而参与HTN的发生与进展过程。本研究对HTN中的线粒体自噬进行了全面系统的分析，有助于加深学界对HTN发病机制、诊断策略及治疗靶点的认识。