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基于加权基因共表达网络分析鉴定与严重烧伤免疫浸润细胞相关的重要模块和生物标志物

Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis.

作者信息

Zhang Zexin, He Yan, Lin Rongjie, Lan Junhong, Fan Yueying, Wang Peng, Jia Chiyu

机构信息

Department of Burns and Plastic and Wound Repair Surgery, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.

Department of Orthopedics, The 900th Hospital of Joint Logistic Support Force, Fuzhou, China.

出版信息

Front Genet. 2022 Jun 9;13:908510. doi: 10.3389/fgene.2022.908510. eCollection 2022.

Abstract

Immunosuppression is an important trigger for infection and a significant cause of death in patients with severe burns. Nevertheless, the prognostic value of immune-related genes remains unclear. This study aimed to identify the biomarkers related to immunosuppression in severe burns. The gene expression profile and clinical data of 185 burn and 75 healthy samples were obtained from the GEO database. Immune infiltration analysis and gene set variation analysis were utilized to identify the disorder of circulating immune cells. A weighted gene co-expression network analysis (WGCNA) was carried out to select immune-related gene modules. Enrichment analysis and protein-protein interaction (PPI) network were performed to select hub genes. Next, LASSO and logistic regression were utilized to construct the hazard regression model with a survival state. Finally, we investigated the correlation between high- and low-risk patients in total burn surface area (TBSA), age, and inhalation injury. Gene set variation analysis (GSVA) and immune infiltration analysis showed that neutrophils increased and T cells decreased in severe burns. In WGCNA, four modular differently expressed in burns and controls were related to immune cells. Based on PPI and enrichment analysis, 210 immune-related genes were identified, mainly involved in T-cell inhibition and neutrophil activation. In LASSO and logistic regression, we screened out key genes, including and and . In the ROC analysis, the area under the curve (AUC) of key genes was 0.945, indicating that the key genes had excellent diagnostic value. Finally, we discovered that the key genes were related to T cells, and the regression model performed well when accompanied by TBSA and age. We identified LCK, SKAP1, GZMB, and LY9 as good prognostic biomarkers that may play a role in post-burn immunosuppression against T-cell dysfunction and as potential immunotherapeutic targets for transformed T-cell dysfunction.

摘要

免疫抑制是严重烧伤患者感染的重要诱因和死亡的重要原因。然而,免疫相关基因的预后价值仍不清楚。本研究旨在识别与严重烧伤免疫抑制相关的生物标志物。从基因表达综合数据库(GEO数据库)获取了185例烧伤样本和75例健康样本的基因表达谱及临床数据。利用免疫浸润分析和基因集变异分析来识别循环免疫细胞的紊乱情况。进行加权基因共表达网络分析(WGCNA)以选择免疫相关基因模块。进行富集分析和蛋白质-蛋白质相互作用(PPI)网络分析以选择枢纽基因。接下来,利用LASSO回归和逻辑回归构建具有生存状态的风险回归模型。最后,我们研究了高风险和低风险患者在烧伤总面积(TBSA)、年龄和吸入性损伤方面的相关性。基因集变异分析(GSVA)和免疫浸润分析表明,严重烧伤患者中性粒细胞增加而T细胞减少。在WGCNA中,烧伤组和对照组中四个差异表达的模块与免疫细胞有关。基于PPI和富集分析,鉴定出210个免疫相关基因,主要涉及T细胞抑制和中性粒细胞活化。在LASSO回归和逻辑回归中,我们筛选出了关键基因,包括 和 以及 。在ROC分析中,关键基因的曲线下面积(AUC)为0.945,表明关键基因具有优异的诊断价值。最后,我们发现关键基因与T细胞有关,并且当伴有TBSA和年龄时,回归模型表现良好。我们确定LCK、SKAP1、颗粒酶B(GZMB)和LY9是良好的预后生物标志物,它们可能在烧伤后针对T细胞功能障碍的免疫抑制中发挥作用,并作为转化T细胞功能障碍的潜在免疫治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8918/9218676/090fae5fae50/fgene-13-908510-g001.jpg

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