Wang Yingwei, Wang Le, Zhang Yan, Wang Minghui, Zhao Huaying, Huang Cheng, Cai Huaiyang, Mo Shuangyang
Clinical Nutrition/Gastroenterology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China.
Department of Gastroenterology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China.
Front Med (Lausanne). 2025 Jun 23;12:1606430. doi: 10.3389/fmed.2025.1606430. eCollection 2025.
Chemokines and neutrophil extracellular trap formation (NETosis) are critical drivers of inflammatory responses. However, the molecular characteristics and interaction mechanisms of these processes in sarcopenia remain incompletely understood.
Utilizing the mRNA expression profile dataset GSE226151 (including 19 sarcopenia, 19 pre-sarcopenia, and 20 healthy control samples), enrichment analysis was performed to identify differentially expressed NETosis-related genes (DENRGs) and chemokine-related genes (DECRGs). Two machine learning algorithms and univariate analysis were integrated to screen signature genes, which were subsequently used to construct diagnostic nomogram models for sarcopenia. Single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were used to investigate pathway associations, followed by the construction of a gene interaction network.
A total of 7 DECRGs and DENRGs were identified, primarily enriched in chemokine signaling pathways, cytokine-cytokine receptor interactions, and sarcopenia-related diseases. Machine learning and univariate analysis revealed three signature genes (CXCR1, CXCR2, and LPL). The nomogram models demonstrated high predictive accuracy in distinguishing sarcopenia from both healthy and pre-sarcopenic states, as evidenced by AUC values of 0.837 (95% CI 0.703-0.947) and 0.903 (95% CI 0.789-0.989), respectively. Single-gene GSEA highlighted significant associations between these genes and the JAK-STAT and PPAR signaling pathways. GSVA indicated that sarcopenia was closely linked to upregulated chemokine signaling, cytokine-receptor interaction activities, and leukocyte transendothelial migration.
The research pinpointed three genes associated with chemokines and NETosis (CXCR1, CXCR2, LPL) and developed highly accurate diagnostic models, offering a new and preliminary approach to differentiate sarcopenia and its early stages.
趋化因子和中性粒细胞胞外诱捕网形成（NETosis）是炎症反应的关键驱动因素。然而，这些过程在肌肉减少症中的分子特征和相互作用机制仍未完全明确。
利用mRNA表达谱数据集GSE226151（包括19例肌肉减少症、19例肌肉减少症前期和20例健康对照样本），进行富集分析以鉴定差异表达的NETosis相关基因（DENRGs）和趋化因子相关基因（DECRGs）。整合两种机器学习算法和单变量分析来筛选特征基因，随后将其用于构建肌肉减少症的诊断列线图模型。采用单基因基因集富集分析（GSEA）和基因集变异分析（GSVA）来研究通路关联，接着构建基因相互作用网络。
共鉴定出7个DECRGs和DENRGs，主要富集于趋化因子信号通路、细胞因子-细胞因子受体相互作用以及与肌肉减少症相关的疾病。机器学习和单变量分析揭示了3个特征基因（CXCR1、CXCR2和LPL）。列线图模型在区分肌肉减少症与健康状态和肌肉减少症前期状态时显示出较高的预测准确性，AUC值分别为0.837（95%CI 0.703-0.947）和0.903（95%CI 0.789-0.989）。单基因GSEA突出了这些基因与JAK-STAT和PPAR信号通路之间的显著关联。GSVA表明，肌肉减少症与趋化因子信号上调、细胞因子-受体相互作用活性以及白细胞跨内皮迁移密切相关。
该研究确定了3个与趋化因子和NETosis相关的基因（CXCR1、CXCR2、LPL）并开发了高度准确的诊断模型，为鉴别肌肉减少症及其早期阶段提供了一种新的初步方法。
