使用生物信息学和药物基因组学方法鉴定脓毒症中免疫相关基因诊断模型及潜在药物
Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches.
作者信息
Chen Peng, Chen Juan, Ye Jinghe, Yang Limin
机构信息
Department of Urology, General Hospital of Northern Theater Command PLA, Shenyang, People's Republic of China.
Department of Oncology, General Hospital of Northern Theater Command PLA, Shenyang, People's Republic of China.
出版信息
Infect Drug Resist. 2023 Aug 28;16:5665-5680. doi: 10.2147/IDR.S418176. eCollection 2023.
PURPOSE
Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis.
PATIENTS AND METHODS
The GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein-protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR).
RESULTS
Totally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway.
CONCLUSION
The 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis.
目的
脓毒症是一种死亡率很高的器官功能障碍。早期识别、诊断和有效治疗脓毒症有利于患者存活。本研究旨在寻找潜在的诊断和免疫相关基因以及药物靶点，为脓毒症提供新的诊断和治疗标志物。
患者与方法
整合GSE69063、GSE154918和GSE28750数据集以评估免疫浸润，并鉴定差异表达基因（DEG）和免疫相关基因。应用加权基因共表达网络分析（WGCNA）来寻找与免疫评分和脓毒症相关的枢纽模块。通过DEG、免疫相关基因和枢纽模块中的基因相互作用筛选出免疫相关关键基因。采用蛋白质-蛋白质相互作用（PPI）分析进一步筛选免疫相关枢纽基因，随后基于免疫相关枢纽基因构建诊断模型。分别通过David软件和DGIdb数据库对免疫相关枢纽基因进行功能分析和药物预测，随后通过逆转录聚合酶链反应（RT-PCR）进行表达验证。
结果
在WGCNA枢纽模块的561个DEG、1793个免疫相关基因和12459个基因之间，共鉴定出93个免疫相关关键基因。通过PPI分析，进一步获得了总共5个诊断和免疫相关枢纽基因，包括IL7R、IL10、CD40LG、CD28和LCN2。确定了这5个基因与免疫细胞之间的关系对，包括LCN2/IL7R/CD28激活的树突状细胞和IL10未成熟B细胞。基于药物基因组学，17种候选药物可能与IL 10相互作用，包括环孢素。6种候选药物可能与CD28相互作用，11种与CD40LG相互作用，CD40LG和CD28是阿地白介素的药物靶点。鉴定出4条显著富集的信号通路，如T细胞受体信号通路、NF-κB信号通路和JAK-STAT信号通路。
结论
5基因诊断模型可用于诊断和指导脓毒症的临床免疫治疗。
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