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生物信息学方法鉴定与 COVID-19 和特发性肺纤维化相关的枢纽基因。

Bioinformatics approach to identify the hub gene associated with COVID-19 and idiopathic pulmonary fibrosis.

机构信息

Department of Respiration, The Fourth Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang, China.

Department of Respiration, Hainan Cancer Hospital, Haikou, Hainan, China.

出版信息

IET Syst Biol. 2023 Dec;17(6):336-351. doi: 10.1049/syb2.12080. Epub 2023 Oct 9.

DOI:10.1049/syb2.12080
PMID:37814484
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10725713/
Abstract

The coronavirus disease 2019 (COVID-19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS-CoV-2 infection, deserves attention. As COVID-19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID-19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID-19. A risk prediction model was developed to assess the prognosis of patients infected with SARS-CoV-2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS-CoV-2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID-19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID-19. With the increasing availability of COVID-19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.

摘要

新型冠状病毒病 2019（COVID-19）已发展成为全球卫生危机。肺纤维化是 SARS-CoV-2 感染的并发症之一，值得关注。由于 COVID-19 是一种不断发展的新临床实体，许多疾病方面仍然未知。COVID-19 和特发性肺纤维化的数据集从基因表达综合数据库中获得。根据 COVID-19 患者的严重程度，使用随机森林（RF）算法筛选出枢纽基因。建立了一种风险预测模型来评估 SARS-CoV-2 感染患者的预后，该模型通过另一个数据集进行了评估。六个基因（命名为 NELL2、GPR183、S100A8、ALPL、CD177 和 IL1R2）可能与重症 SARS-CoV-2 感染患者 PF 的发展有关。S100A8 被认为是一个重要的靶基因，与 COVID-19 和肺纤维化密切相关。成功地构建了神经网络模型来预测 COVID-19 患者的预后。随着 COVID-19 数据集的日益普及，生物信息学方法可为该疾病的诊断、治疗和预后提供可能的预测靶点，并为临床药物和疫苗的开发提供干预方向。

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