Bai Wenmei, Li Fengsen
The Fourth Clinical College of Xinjiang Medical University, Urumqi, China.
Department of Respiratory and Critical Care Medicine, Fourth Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Medicine (Baltimore). 2025 Aug 29;104(35):e44209. doi: 10.1097/MD.0000000000044209.
Long COVID (LC) poses ongoing public health challenges due to its persistent symptoms following severe acute respiratory syndrome coronavirus 2 infection. Early identification of at-risk individuals remains difficult, and molecular biomarkers are urgently needed. This study aimed to explore the role of N7-methylguanosine (m7G) methylation-related regulatory genes in LC pathogenesis and to develop a predictive model for early detection. Gene expression profiles of LC patients were obtained from the GEO database (GSE224615), and differentially expressed genes (DEGs) were identified. These DEGs were intersected with m7G regulatory genes to identify LC-specific candidates. A protein-protein interaction network was constructed to identify hub genes, and enrichment analyses including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were performed to investigate the biological relevance of the identified genes. Immune cell infiltration analyses were conducted to explore the immunological features associated with candidate genes. Findings were validated using an external dataset (GSE217948). A clinical prediction model was constructed using Least absolute shrinkage and selection operator regression followed by logistic regression, and evaluated via receiver operating characteristic curve, calibration, and decision curve analysis. A total of 65 DEGs were identified in LC patients, comprising 44 up-regulated and 21 down-regulated genes. Thirty genes overlapped with the m7G regulatory gene set. Functional enrichment revealed significant involvement in pathways such as FceRI-mediated NF-κB activation and platelet aggregation. Correlation analysis showed that several m7G-related genes were associated with altered immune cell infiltration patterns. The external dataset confirmed the reproducibility of gene expression trends. Seven core genes were ultimately selected to build the predictive model, which demonstrated robust performance in distinguishing LC patients from controls. This study highlights the importance of m7G methylation in LC pathogenesis and uncovers novel immune-related mechanisms underlying its persistence. The predictive model based on m7G-related markers provides a promising tool for early LC identification and may inform future diagnostic and therapeutic strategies.
长期新冠（LC）由于严重急性呼吸综合征冠状病毒2感染后持续出现症状，给公共卫生带来了持续挑战。早期识别高危个体仍然困难，迫切需要分子生物标志物。本研究旨在探讨N7-甲基鸟苷（m7G）甲基化相关调控基因在LC发病机制中的作用，并建立早期检测的预测模型。从基因表达综合数据库（GEO数据库，GSE224615）获取LC患者的基因表达谱，并鉴定差异表达基因（DEGs）。将这些DEGs与m7G调控基因进行交叉分析，以鉴定LC特异性候选基因。构建蛋白质-蛋白质相互作用网络以识别枢纽基因，并进行包括基因本体论、京都基因与基因组百科全书以及基因集富集分析在内的富集分析，以研究已鉴定基因的生物学相关性。进行免疫细胞浸润分析以探索与候选基因相关的免疫学特征。使用外部数据集（GSE217948）对研究结果进行验证。使用最小绝对收缩和选择算子回归，随后进行逻辑回归构建临床预测模型，并通过受试者工作特征曲线、校准和决策曲线分析进行评估。在LC患者中总共鉴定出65个DEGs，包括44个上调基因和21个下调基因。30个基因与m7G调控基因集重叠。功能富集显示这些基因显著参与如FcεRI介导的NF-κB激活和血小板聚集等通路。相关性分析表明，几个与m7G相关的基因与免疫细胞浸润模式改变有关。外部数据集证实了基因表达趋势的可重复性。最终选择7个核心基因构建预测模型，该模型在区分LC患者和对照方面表现出强大性能。本研究强调了m7G甲基化在LC发病机制中的重要性，并揭示了其持续性背后新的免疫相关机制。基于m7G相关标志物的预测模型为LC的早期识别提供了一个有前景的工具，并可能为未来的诊断和治疗策略提供参考。
