■当败血症与严重低血压有关并导致大量死亡时,就会发生败血症性休克。感染性休克的早期诊断对于降低死亡率至关重要。高质量的生物标志物可以作为准确预测疾病诊断的指标进行客观测量和评估。然而,单基因预测效率不足;因此,我们确定了基于基因签名的风险评分模型,以提高预测效率.
■从基因表达综合(GEO)数据库下载GSE33118和GSE26440的基因表达谱。这两个数据集被合并,并使用R软件中的limma包鉴定差异表达基因(DEGs)。进行了DEGs的基因本体论(GO)和京都基因和基因组百科全书(KEGG)途径富集。随后,将Lasso回归和Boruta特征选择算法结合起来识别感染性休克的hub基因。然后对GSE9692进行加权基因共表达网络分析(WGCNA)以鉴定脓毒性休克相关基因模块。随后,这些模块中与感染性休克相关的DEGs匹配的基因被鉴定为感染性休克的中心基因.为了进一步了解hub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.采用受试者工作特征(ROC)分析确定hub基因在感染性休克中的诊断价值,并采用定量PCR(qPCR)和Westernblotting对我院感染性休克患者进行验证。
■在GSE33118和GSE26440数据库中总共获得了975个DEG,其中30个DEG显著上调。利用Lasso回归和Boruta特征选择算法,六个hub基因(CD177、CLEC5A、在感染性休克中具有表达差异的CYSTM1、MCEMP1、MMP8和RGL4)被筛选为感染性休克的潜在诊断标志物,并在GSE9692数据集中进一步验证。WGCNA用于鉴定共表达模块和模块-性状相关性。富集分析显示活性氧途径显著富集,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。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巨噬细胞的浸润,激活的肥大细胞,中性粒细胞,CD8T细胞,而幼稚B细胞在脓毒性休克组更为显著。此外,CD177、CLEC5A、与健康供者相比,在败血性休克患者的外周血单核细胞(PBMC)中观察到CYSTM1,MCEMP1,MMP8和RGL4信使RNA(mRNA)。与对照组参与者相比,在从脓毒性休克患者分离的PBMC中也观察到更高的CD177和MMP8蛋白表达水平。
■CD177,CLEC5A,CYSTM1,MCEMP1,MMP8和RGL4被鉴定为hub基因,对感染性休克患者的早期诊断具有重要价值。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。
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 (CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4) 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 CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4 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.
CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4 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.