Sun Qinghui, Zhang Hai-Li, Wang Yichao, Xiu Hao, Lu Yufei, He Na, Yin Li
School of Tropical Medicine, Hainan Medical University, Haikou 571199, China.
NHC Key Laboratory of Tropical Disease Control, School of Tropical Medicine, Hainan Medical University, Haikou 571199, China.
Int J Mol Sci. 2025 May 7;26(9):4433. doi: 10.3390/ijms26094433.
Sepsis is a life-threatening condition driven by dysregulated immune responses, resulting in organ dysfunction and high mortality rates. Identifying key genes and pathways involved in sepsis progression is crucial for improving diagnostic and therapeutic strategies. This study analyzed transcriptomic data from 49 samples (37 septic patients across days 0, 1, and 8, and 12 healthy controls) using weighted gene co-expression network analysis (WGCNA) and multi-algorithm feature selection approaches. Differential expression analysis, pathway enrichment, and network analyses were employed to uncover potential biomarkers and molecular mechanisms. WGCNA identified modules such as MEbrown4 and MEblack, which strongly correlated with sepsis progression (r > 0.7, < 0.01). Differential expression analysis highlighted up-regulated genes like CD177 and down-regulated genes like LOC440311. KEGG analysis revealed significant pathways including neuroactive ligand-receptor interaction, PI3K-Akt signaling, and MAPK signaling. Gene ontology analysis showed involvement in immune-related processes such as complement activation and antigen binding. Protein-protein interaction network analysis identified hub genes such as TNFSF10, IGLL5, BCL2L1, and SNCA. Feature selection methods (random forest, LASSO regression, SVM-RFE) consistently identified top predictors like TMCC2, TNFSF10, and PLVAP. Receiver operating characteristic (ROC) analysis demonstrated high predictive accuracy for sepsis progression, with AUC values of 0.973 (TMCC2), 0.969 (TNFSF10), and 0.897 (PLVAP). Correlation analysis linked key genes such as TNFSF10, GUCD1, and PLVAP to pathways involved in immune response, cell death, and inflammation. This integrative transcriptomic analysis identifies critical gene modules, pathways, and biomarkers associated with sepsis progression. Key genes like TNFSF10, TMCC2, and PLVAP genes show strong diagnostic potential, providing novel insights into sepsis pathogenesis and offering promising targets for future therapeutic interventions. Among these, TNFSF10 and PLVAP are known to encode secreted proteins, suggesting their potential as circulating biomarkers. This enhances their translational relevance in clinical diagnostics.
脓毒症是一种由免疫反应失调驱动的危及生命的病症，会导致器官功能障碍和高死亡率。识别参与脓毒症进展的关键基因和通路对于改进诊断和治疗策略至关重要。本研究使用加权基因共表达网络分析（WGCNA）和多算法特征选择方法，分析了49个样本（37例脓毒症患者在第0天、第1天和第8天的样本，以及12例健康对照）的转录组数据。采用差异表达分析、通路富集和网络分析来揭示潜在的生物标志物和分子机制。WGCNA识别出了MEbrown4和MEblack等模块，它们与脓毒症进展密切相关（r>0.7，<0.01）。差异表达分析突出了如CD177等上调基因和如LOC440311等下调基因。KEGG分析揭示了包括神经活性配体-受体相互作用、PI3K-Akt信号传导和MAPK信号传导等显著通路。基因本体分析表明其参与了免疫相关过程，如补体激活和抗原结合。蛋白质-蛋白质相互作用网络分析确定了TNFSF10、IGLL5、BCL2L1和SNCA等枢纽基因。特征选择方法（随机森林、LASSO回归、支持向量机递归特征消除）一致确定了TMCC2、TNFSF10和PLVAP等顶级预测因子。受试者工作特征（ROC）分析表明对脓毒症进展具有较高的预测准确性，TMCC2的AUC值为0.973，TNFSF10为0.969，PLVAP为0.897。相关性分析将TNFSF10、GUCD1和PLVAP等关键基因与免疫反应、细胞死亡和炎症相关的通路联系起来。这种综合转录组分析确定了与脓毒症进展相关的关键基因模块、通路和生物标志物。TNFSF10、TMCC2和PLVAP等关键基因具有很强的诊断潜力，为脓毒症发病机制提供了新的见解，并为未来的治疗干预提供了有希望的靶点。其中，TNFSF10和PLVAP已知编码分泌蛋白，表明它们作为循环生物标志物的潜力。这增强了它们在临床诊断中的转化相关性。
