Emergency Department, The Third People's Hospital of Hefei, Hefei, China.
Eur Rev Med Pharmacol Sci. 2023 Jan;27(2):805-817. doi: 10.26355/eurrev_202301_31082.
Coronavirus disease 2019 (COVID-19) is a highly contagious infectious disease caused by the newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Severe COVID-19 infection causes complications in the respiratory tract, which results in pulmonary failure, thus requiring prolonged mechanical ventilation (MV). An increase in the number of patients with COVID-19 poses numerous challenges to the healthcare system, including the shortage of MV facilities. Despite continued efforts to improve COVID-19 diagnosis and treatment, no study has established a reliable predictive model for the risk assessment of deteriorating COVID-19 cases.
We extracted the expression profiles and clinical data of the GSE157103, GSE116560 and GSE21802 cohorts from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified as the intersection of the resulting differential genes as analysed via limma, edgeR and DESeq2 R packages. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the R package 'clusterProfiler'. Variables closely related to MV were examined using univariate Cox regression analysis, and significant variables were subjected to least absolute shrinkage and selection operator regression (LASSO) analysis for the construction of a risk model. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were generated to verify the predictive values of the risk model.
We identified 198 unigenes that were differentially expressed in COVID-19 samples. Moreover, a five-gene signature (BTN3A1, GPR35, HAAO, SLC2A6 and TEX2) was constructed to predict the ventilator-free days of patients with COVID-19. In our study, we used the five-gene signature to calculate the risk score (MV score) for each patient. The results revealed a statistical correlation between the MV score and the scores of the Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment of patients with COVID-19. Kaplan-Meier analysis revealed that the number of ventilator-free days was significantly reduced in the low-MVscore group compared to the high-MVscore group. The ROC curves revealed that our model had a good performance, and the areas under the ROC curve were 0.93 (3-week ROC) and 0.97 (4-week ROC). The 'Limma' package analysis revealed 71 upregulated genes and 59 downregulated genes in the high-MV score group compared to the low-MV score group. These DEGs were mainly enriched in cytokine signalling in immune system and cellular response to cytokine stimulus.
This study identified a five-gene signature that can predict the length of ventilator-free days for patients with COVID-19.
新型冠状病毒病（COVID-19）是一种由新型严重急性呼吸系统综合征冠状病毒 2（SARS-CoV-2）引起的高传染性传染病。严重的 COVID-19 感染会导致呼吸道并发症，从而导致肺衰竭，因此需要长时间的机械通气（MV）。COVID-19 患者数量的增加给医疗系统带来了诸多挑战，包括 MV 设备的短缺。尽管不断努力改进 COVID-19 的诊断和治疗，但尚无研究建立可靠的预测模型来评估 COVID-19 病情恶化的风险。
我们从基因表达综合数据库中提取了 GSE157103、GSE116560 和 GSE21802 队列的表达谱和临床数据。通过 limma、edgeR 和 DESeq2 R 包分析，确定差异表达基因（DEGs）为差异基因的交集。使用 R 包“clusterProfiler”进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析。使用单变量 Cox 回归分析检查与 MV 密切相关的变量，然后对显著变量进行最小绝对收缩和选择算子回归（LASSO）分析，以构建风险模型。生成 Kaplan-Meier 分析和接收器工作特征（ROC）曲线，以验证风险模型的预测值。
我们鉴定了 198 个在 COVID-19 样本中差异表达的基因。此外，构建了一个由 5 个基因（BTN3A1、GPR35、HAAO、SLC2A6 和 TEX2）组成的signature，用于预测 COVID-19 患者的无呼吸机天数。在本研究中，我们使用 5 个基因 signature 计算每位患者的风险评分（MV 评分）。结果表明，MV 评分与 COVID-19 患者急性生理学和慢性健康评估以及序贯器官衰竭评估的评分之间存在统计学相关性。Kaplan-Meier 分析显示，低 MV 评分组的无呼吸机天数明显少于高 MV 评分组。ROC 曲线显示，我们的模型具有良好的性能，ROC 曲线下面积为 0.93（3 周 ROC）和 0.97（4 周 ROC）。“Limma”包分析显示，与低 MV 评分组相比，高 MV 评分组有 71 个上调基因和 59 个下调基因。这些差异表达基因主要富集于免疫系统细胞因子信号转导和细胞对细胞因子刺激的反应。
本研究鉴定了一个可以预测 COVID-19 患者无呼吸机天数的 5 个基因 signature。
