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基于基因表达谱的脓毒症分子亚型及核心基因综合分析

Comprehensive Analysis of Molecular Subtypes and Hub Genes of Sepsis by Gene Expression Profiles.

作者信息

Lai Yongxing, Lin Chunjin, Lin Xing, Wu Lijuan, Zhao Yinan, Shao Tingfang, Lin Fan

机构信息

Department of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.

Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, China.

出版信息

Front Genet. 2022 Aug 12;13:884762. doi: 10.3389/fgene.2022.884762. eCollection 2022.

Abstract

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. 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. 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. 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.

摘要

脓毒症是一种具有临床症状异质性的全身炎症反应综合征(SIRS)。迫切需要进一步探索脓毒症分子亚型并阐明其潜在机制的研究。从基因表达综合数据库(GEO)下载脓毒症外周血微阵列数据集,并鉴定差异表达基因(DEG)。进行加权基因共表达网络分析(WGCNA)以筛选关键模块基因。进行共识聚类分析以识别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。之后,我们对亚型相关、显著表达和模块特异性基因进行交集分析,以筛选共识DEG(共DEG)。进行富集分析以识别关键途径。使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。脓毒症患者被分为三个聚类。GSVA显示脓毒症不同聚类中的这些DEG被分配到代谢、氧化磷酸化、自噬调节和VEGF途径等。此外,我们鉴定了40个共DEG和几个失调途径。一个具有25个基因特征的诊断模型被证明对脓毒症的诊断具有高价值。在外部数据集中AUC值大于0.95的诊断模型中的基因被筛选为脓毒症诊断的关键基因。最后,ANKRD22、GPR84、GYG1、BLOC1S1、CARD11、NOG和LRG1被识别为与脓毒症分子亚型相关的关键基因。脓毒症不同分子亚组之间存在显著差异和富集途径,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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a5/9412106/ba85cbf37293/fgene-13-884762-g001.jpg

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