Consensus analysis via weighted gene co-expression network analysis (WGCNA) reveals genes participating in early phase of acute respiratory distress syndrome (ARDS) induced by sepsis

The understanding of mechanism during conversion from sepsis to sepsis-related ARDS remains limited. In this study, we collected gene expression matrix from the Gene Expression Omnibus (GEO) database and constructed networks using weighted gene co-expression network analysis (WGCNA) to identify the consensus and opposite modules between sepsis and sepsis-induced ARDS and obtained 27 consensus modules associated with sepsis and sepsis-related ARDS, including one model (160 genes) with opposite correlation and 1 sepsis-ARDS specific model with 34 genes. Differentially expressed genes analysis, functional enrichment and protein-protein interactions analyses of candidate genes were performed; 15 of these genes showed different expressions in sepsis-induced ARDS patients, compared with sepsis patients; genes were enriched in processes associated with ribosome, tissue mechanics and extracellular matrix. Feature selection analysis revealed that three genes, TLCD4, PRSS30P, and ZNF493, showed moderate performance in identifying sepsis-induced ARDS from sepsis. Ribosome-related genes indicate crucial roles in the development of sepsis-induced ARDS.

脓毒症向脓毒症相关急性呼吸窘迫综合征（Acute Respiratory Distress Syndrome, ARDS）转化过程中的机制阐释仍存在诸多局限。本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取基因表达矩阵，采用加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）构建共表达网络，以识别脓毒症与脓毒症诱导型ARDS之间的共有模块与负相关模块；最终获得27个与脓毒症及脓毒症相关ARDS相关的共有模块，其中包含1个呈相反相关性的基因模块（含160个基因）以及1个脓毒症-ARDS特异性基因模块（含34个基因）。对候选基因开展差异表达分析、功能富集分析及蛋白质相互作用分析，结果显示相较于单纯脓毒症患者，脓毒症诱导型ARDS患者体内有15个上述基因存在显著表达差异；富集分析结果表明，这些基因主要富集于核糖体、组织力学及细胞外基质相关生物学过程。特征选择分析结果显示，TLCD4、PRSS30P与ZNF493这3个基因在区分脓毒症与脓毒症诱导型ARDS方面表现出中等区分效能。核糖体相关基因在脓毒症诱导型ARDS的发生发展过程中发挥关键作用。