Department of Anesthesiology and Critical Care Medicine, Tianjin Nankai Hospital, Tianjin Medical University, Tianjin, China.
Department of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Front Immunol. 2023 Jun 8;14:1184700. doi: 10.3389/fimmu.2023.1184700. eCollection 2023.
Septic shock occurs when sepsis is related to severe hypotension and leads to a remarkable high number of deaths. The early diagnosis of septic shock is essential to reduce mortality. High-quality biomarkers can be objectively measured and evaluated as indicators to accurately predict disease diagnosis. However, single-gene prediction efficiency is inadequate; therefore, we identified a risk-score model based on gene signature to elevate predictive efficiency.
The gene expression profiles of GSE33118 and GSE26440 were downloaded from the Gene Expression Omnibus (GEO) database. These two datasets were merged, and the differentially expressed genes (DEGs) were identified using the limma package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed. Subsequently, Lasso regression and Boruta feature selection algorithm were combined to identify the hub genes of septic shock. GSE9692 was then subjected to weighted gene co-expression network analysis (WGCNA) to identify the septic shock-related gene modules. Subsequently, the genes within such modules that matched with septic shock-related DEGs were identified as the hub genes of septic shock. To further understand the function and signaling pathways of hub genes, we performed gene set variation analysis (GSVA) and then used the CIBERSORT tool to analyze the immune cell infiltration pattern of diseases. The diagnostic value of hub genes in septic shock was determined using receiver operating characteristic (ROC) analysis and verified using quantitative PCR (qPCR) and Western blotting in our hospital patients with septic shock.
A total of 975 DEGs in the GSE33118 and GSE26440 databases were obtained, of which 30 DEGs were remarkably upregulated. With the use of Lasso regression and Boruta feature selection algorithm, six hub genes (, , , , , and ) with expression differences in septic shock were screened as potential diagnostic markers for septic shock among the significant DEGs and were further validated in the GSE9692 dataset. WGCNA was used to identify the co-expression modules and module-trait correlation. Enrichment analysis showed significant enrichment in the reactive oxygen species pathway, hypoxia, phosphatidylinositol 3-kinases (PI3K)/Protein Kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling, nuclear factor-κβ/tumor necrosis factor alpha (NF-κβ/TNF-α), and interleukin-6 (IL-6)/Janus Kinase (JAK)/Signal Transducers and Activators of Transcription 3 (STAT3) signaling pathways. The receiver operating characteristic curve (ROC) of these signature genes was 0.938, 0.914, 0.939, 0.956, 0.932, and 0.914, respectively. In the immune cell infiltration analysis, the infiltration of M0 macrophages, activated mast cells, neutrophils, CD8 T cells, and naive B cells was more significant in the septic shock group. In addition, higher expression levels of , and messenger RNA (mRNA) were observed in peripheral blood mononuclear cells (PBMCs) isolated from septic shock patients than from healthy donors. Higher expression levels of CD177 and MMP8 proteins were also observed in the PBMCs isolated from septic shock patients than from control participants.
, , , , , and were identified as hub genes, which were of considerable value in the early diagnosis of septic shock patients. These preliminary findings are of great significance for studying immune cell infiltration in the pathogenesis of septic shock, which should be further validated in clinical studies and basic studies.
当败血症与严重低血压相关并导致大量死亡时，就会发生感染性休克。早期诊断感染性休克对于降低死亡率至关重要。高质量的生物标志物可以客观地测量和评估，作为准确预测疾病诊断的指标。然而，单个基因的预测效率不足；因此，我们基于基因特征识别出风险评分模型，以提高预测效率。
从基因表达综合数据库（GEO）下载 GSE33118 和 GSE26440 的基因表达谱。使用 R 软件中的 limma 包合并这两个数据集，并识别差异表达基因（DEGs）。进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析 DEGs。随后，结合 Lasso 回归和 Boruta 特征选择算法，识别感染性休克的枢纽基因。然后，使用加权基因共表达网络分析（WGCNA）对 GSE9692 进行分析，以识别与感染性休克相关的基因模块。随后，识别与感染性休克相关的 DEGs 相匹配的基因模块中的基因作为感染性休克的枢纽基因。为了进一步了解枢纽基因的功能和信号通路，我们进行了基因集变异分析（GSVA），然后使用 CIBERSORT 工具分析疾病的免疫细胞浸润模式。使用接收器操作特征（ROC）分析确定枢纽基因在感染性休克中的诊断价值，并通过我们医院感染性休克患者的定量 PCR（qPCR）和 Western blot 进行验证。
从 GSE33118 和 GSE26440 数据库中获得了 975 个 DEGs，其中 30 个 DEGs明显上调。使用 Lasso 回归和 Boruta 特征选择算法，从显著的 DEGs 中筛选出 6 个枢纽基因（、、、、、和），这些基因在感染性休克中存在表达差异，可作为感染性休克的潜在诊断标志物，并在 GSE9692 数据集得到进一步验证。使用 WGCNA 识别共表达模块和模块-特征相关性。富集分析显示，在活性氧途径、缺氧、磷脂酰肌醇 3-激酶（PI3K）/蛋白激酶 B（AKT）/哺乳动物雷帕霉素靶蛋白（mTOR）信号、核因子-κβ/肿瘤坏死因子-α（NF-κβ/TNF-α）和白细胞介素-6（IL-6）/Janus 激酶（JAK）/信号转导和转录激活因子 3（STAT3）信号通路中存在显著富集。这些特征基因的受试者工作特征曲线（ROC）分别为 0.938、0.914、0.939、0.956、0.932 和 0.914。在免疫细胞浸润分析中，M0 巨噬细胞、激活的肥大细胞、中性粒细胞、CD8 T 细胞和幼稚 B 细胞在感染性休克组中的浸润更为显著。此外，从感染性休克患者外周血单核细胞（PBMCs）中分离出的细胞中观察到更高水平的、和信使 RNA（mRNA）的表达。从感染性休克患者分离的 PBMCs 中也观察到 CD177 和 MMP8 蛋白的表达水平高于对照组。
、、、、、和 被鉴定为枢纽基因，它们在感染性休克患者的早期诊断中具有重要价值。这些初步发现对于研究感染性休克发病机制中的免疫细胞浸润具有重要意义，应在临床研究和基础研究中进一步验证。
