Comprehensive Analysis of Molecular Subtypes and Hub Genes of Sepsis by Gene Expression Profiles.
Comprehensive Analysis of Molecular Subtypes and Hub Genes of Sepsis by Gene Expression Profiles.
复制标题
DOI：
10.3389/fgene.2022.884762
复制
发表时间：
2022
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
背景：脓毒症是一种具有异质性临床症状的全身炎症反应综合征（SIRS）。因此，迫切需要进一步研究脓毒症的分子亚型并阐明其可能的机制。 研究方法：从基因表达综合数据库（GEO）下载脓毒症患者外周血的微阵列数据集，并鉴定差异表达基因（DEG）。采用加权基因共表达网络分析（WGCNA）筛选关键模块基因。进行一致聚类分析以鉴定不同的脓毒症分子亚型。使用基因集变异分析（GSVA）探索亚型特异性途径。随后，我们对亚型相关的、显著表达的和模块特异性的基因进行筛选，以筛选共有DEG（co-DEG）。进行了富集分析，以确定关键途径。采用最小绝对收缩和选择算子（LASSO）回归分析筛选潜在的诊断生物标志物。 结果：脓毒症患者可分为三组。GSVA显示这些DEG在脓毒症的不同集群中被分配到代谢、氧化磷酸化、自噬调节和VEGF途径等。此外，我们还鉴定了40个共DEG和几个失调的途径。具有25个基因特征的诊断模型已被证明对于脓毒症的诊断具有很高的价值。将诊断模型中外部数据集中AUC值大于0.95的基因筛选为脓毒症诊断的关键基因。最后，ANKRD 22、GPR 84、GYG 1、BLOC 1 S1、CARD 11、NOG和LRG 1被认为是与脓毒症分子亚型相关的关键基因。 结论：脓毒症不同分子亚群之间存在显著差异和丰富的通路，这可能是导致脓毒症患者临床症状和预后异质性的关键因素。我们目前的研究为脓毒症分子亚型提供了新的诊断和治疗生物标志物。
Background: Sepsis is a systemic inflammatory response syndrome (SIRS) with heterogeneity of clinical symptoms. Studies further exploring the molecular subtypes of sepsis and elucidating its probable mechanisms are urgently needed. Methods: Microarray datasets of peripheral blood in sepsis were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) analysis was conducted to screen key module genes. Consensus clustering analysis was carried out to identify distinct sepsis molecular subtypes. Subtype-specific pathways were explored using gene set variation analysis (GSVA). Afterward, we intersected subtype-related, dramatically expressed and module-specific genes to screen consensus DEGs (co-DEGs). Enrichment analysis was carried out to identify key pathways. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for screen potential diagnostic biomarkers. Results: Patients with sepsis were classified into three clusters. GSVA showed these DEGs among different clusters in sepsis were assigned to metabolism, oxidative phosphorylation, autophagy regulation, and VEGF pathways, etc. In addition, we identified 40 co-DEGs and several dysregulated pathways. A diagnostic model with 25-gene signature was proven to be of high value for the diagnosis of sepsis. Genes in the diagnostic model with AUC values more than 0.95 in external datasets were screened as key genes for the diagnosis of sepsis. Finally, ANKRD22, GPR84, GYG1, BLOC1S1, CARD11, NOG, and LRG1 were recognized as critical genes associated with sepsis molecular subtypes. Conclusion: There are remarkable differences in and enriched pathways among different molecular subgroups of sepsis, which may be the key factors leading to heterogeneity of clinical symptoms and prognosis in patients with sepsis. Our current study provides novel diagnostic and therapeutic biomarkers for sepsis molecular subtypes.