Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm.
Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm.
复制标题
DOI：
10.1186/s41065-021-00215-8
复制
发表时间：
2022-02-21
期刊：
影响因子：
2.7
通讯作者：
Tu X
中科院分区：
文献类型：
--
作者：
Li M;Huang H;Ke C;Tan L;Wu J;Xu S;Tu X
脓毒症是一种危及生命的疾病，其中免疫反应针对宿主组织，导致器官衰竭。由于脓毒症不存在特定的症状，其诊断往往被延迟。缺乏诊断准确性导致非特异性诊断，迄今为止，仍然缺乏检测患者败血症的标准诊断测试。因此，鉴定脓毒症相关诊断基因至关重要。本研究旨在进行综合分析，以评估脓毒症患者样本和正常样本的免疫评分，然后进行加权基因共表达网络分析（WGCNA），以识别脓毒症中免疫浸润相关基因和潜在的转录组标记物。基于这些免疫浸润相关基因的蛋白质-蛋白质相互作用网络，建立了筛选脓毒症诊断标志物的基因调控网络。此外，我们将WGCNA与支持向量机（SVM）算法相结合，建立了脓毒症的诊断模型。结果显示，脓毒症患者的免疫评分明显低于正常人。与免疫评分呈正相关的基因有328个，与免疫评分呈负相关的基因有333个。使用Cytoscape中的MCODE插件，我们识别出了四个模块，通过功能注释，我们发现这些模块与免疫反应有关。基因本体功能富集分析表明，所识别的基因与中性粒细胞脱颗粒、免疫反应中的中性粒细胞激活、中性粒细胞激活和中性粒细胞介导的免疫等功能相关。京都基因和基因组百科全书（KEGG）通路分析显示了诸如原发性免疫缺陷、Th 1和Th 2细胞分化、T细胞受体信号通路和自然杀伤细胞介导的细胞毒性等通路的富集。最后，我们确定了一个包含枢纽基因LCK、CCL 5、ITGAM和MMP 9的四基因签名，并建立了一个可用于诊断脓毒症患者的模型。在线版本包含补充材料，可通过10.1186/s41065-021-00215-8获得。
Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not present with specific symptoms, its diagnosis is often delayed. The lack of diagnostic accuracy results in a non-specific diagnosis, and to date, a standard diagnostic test to detect sepsis in patients remains lacking. Therefore, it is vital to identify sepsis-related diagnostic genes. This study aimed to conduct an integrated analysis to assess the immune scores of samples from patients diagnosed with sepsis and normal samples, followed by weighted gene co-expression network analysis (WGCNA) to identify immune infiltration-related genes and potential transcriptome markers in sepsis. Furthermore, gene regulatory networks were established to screen diagnostic markers for sepsis based on the protein-protein interaction networks involving these immune infiltration-related genes. Moreover, we integrated WGCNA with the support vector machine (SVM) algorithm to build a diagnostic model for sepsis. Results showed that the immune score was significantly lower in the samples from patients with sepsis than in normal samples. A total of 328 and 333 genes were positively and negatively correlated with the immune score, respectively. Using the MCODE plugin in Cytoscape, we identified four modules, and through functional annotation, we found that these modules were related to the immune response. Gene Ontology functional enrichment analysis showed that the identified genes were associated with functions such as neutrophil degranulation, neutrophil activation in the immune response, neutrophil activation, and neutrophil-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed the enrichment of pathways such as primary immunodeficiency, Th1- and Th2-cell differentiation, T-cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. Finally, we identified a four-gene signature, containing the hub genes LCK, CCL5, ITGAM, and MMP9, and established a model that could be used to diagnose patients with sepsis. The online version contains supplementary material available at 10.1186/s41065-021-00215-8.
登录
查看更多内容
影响因子：
2.7
作者：
Bai, Zhenjiang;Li, Yiping;Fang, Fang
通讯作者：
Fang, Fang
影响因子：
3.7
作者：
Godini, Rasoul;Fallahi, Hossein;Ebrahimie, Esmaeil
通讯作者：
Ebrahimie, Esmaeil
影响因子：
5.8
作者：
Hinchcliff, Emily;Paquette, Cherie;Jazaeri, Amir
通讯作者：
Jazaeri, Amir
影响因子：
76.2
作者：
Scicluna, Brendon P.;van Vught, Lonneke A.;van der Poll, Tom
通讯作者：
van der Poll, Tom
影响因子：
6.7
作者：
Huang, Jiamin;Sun, Ran;Sun, Bingwei
通讯作者：
Sun, Bingwei