Identification of immune-related endoplasmic reticulum stress genes in sepsis using bioinformatics and machine learning.
Identification of immune-related endoplasmic reticulum stress genes in sepsis using bioinformatics and machine learning.
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应用生物信息学和机器学习识别脓毒症免疫相关内质网应激基因。
DOI：
10.3389/fimmu.2022.995974
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发表时间：
2022
影响因子：
7.3
通讯作者：
Liu, Youtan
中科院分区：
文献类型：
--
作者：
Gong, Ting;Liu, Yongbin;Tian, Zhiyuan;Zhang, Min;Gao, Hejun;Peng, Zhiyong;Yin, Shuang;Cheung, Chi Wai;Liu, Youtan
脓毒症诱导的免疫细胞凋亡导致关键免疫效应细胞的广泛耗竭。内质网(ER)应激参与了细胞凋亡途径，但对其在脓毒症相关免疫细胞凋亡中的作用知之甚少。本研究的目的是通过生物信息学和机器学习算法，基于健康对照组和脓毒症患者之间的差异表达基因(Deg)，建立与内质网应激相关的脓毒症预后和诊断标志。包括败血症患者和健康对照的基因表达谱的转录数据集是从GEO数据库下载的。应用新的综合机器学习算法和生物信息学分析，包括功能浓缩分析、共识聚类、加权基因共表达网络分析(WGCNA)和蛋白质-蛋白质相互作用(PPI)网络构建，识别与脓毒症患者相关的免疫相关内质网应激枢纽基因。然后，通过Logistic回归建立诊断模型，并根据显著程度得到脓毒症的分子亚型。最后，从有意义的数据中筛选出脓毒症的潜在诊断标记物，并在多个数据集中进行验证。在正常对照组和脓毒症患者中，浸润性免疫细胞群的类型和丰度有显著差异。免疫相关内质网应激基因对脓毒症患者的预测具有较强的稳定性和较高的准确性。在显著的DEG中，有10个基因被筛选为脓毒症的潜在诊断标志，并在多个数据集中得到进一步验证。此外，从脓毒症患者分离的PBMC中SCAMP5mRNA和蛋白的表达水平也高于健康献血者(n=5)。我们基于机器学习算法和生物信息学建立了一个稳定、准确的特征来评价脓毒症的诊断。SCAMP5被初步确定为脓毒症的诊断标记物，可能通过调节内质网应激而影响其进展。
Sepsis-induced apoptosis of immune cells leads to widespread depletion of key immune effector cells. Endoplasmic reticulum (ER) stress has been implicated in the apoptotic pathway, although little is known regarding its role in sepsis-related immune cell apoptosis. The aim of this study was to develop an ER stress-related prognostic and diagnostic signature for sepsis through bioinformatics and machine learning algorithms on the basis of the differentially expressed genes (DEGs) between healthy controls and sepsis patients. The transcriptomic datasets that include gene expression profiles of sepsis patients and healthy controls were downloaded from the GEO database. The immune-related endoplasmic reticulum stress hub genes associated with sepsis patients were identified using the new comprehensive machine learning algorithm and bioinformatics analysis which includes functional enrichment analyses, consensus clustering, weighted gene coexpression network analysis (WGCNA), and protein-protein interaction (PPI) network construction. Next, the diagnostic model was established by logistic regression and the molecular subtypes of sepsis were obtained based on the significant DEGs. Finally, the potential diagnostic markers of sepsis were screened among the significant DEGs, and validated in multiple datasets. Significant differences in the type and abundance of infiltrating immune cell populations were observed between the healthy control and sepsis patients. The immune-related ER stress genes achieved strong stability and high accuracy in predicting sepsis patients. 10 genes were screened as potential diagnostic markers for sepsis among the significant DEGs, and were further validated in multiple datasets. In addition, higher expression levels of SCAMP5 mRNA and protein were observed in PBMCs isolated from sepsis patients than healthy donors (n = 5). We established a stable and accurate signature to evaluate the diagnosis of sepsis based on the machine learning algorithms and bioinformatics. SCAMP5 was preliminarily identified as a diagnostic marker of sepsis that may affect its progression by regulating ER stress.
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WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
4.6
作者：
Kim, Hee Jung;Jeong, Jae Seok;Kim, So Ri;Park, Seung Yong;Chae, Han Jung;Lee, Yong Chul
通讯作者：
Lee, Yong Chul
DOI：
10.1038/nri3552
发表时间：
2013-12
期刊：
Nature reviews. Immunology
影响因子：
--
作者：
通讯作者：
--
影响因子：
1.6
作者：
Ma, T.;Han, L.;Xue, C.
通讯作者：
Xue, C.