School of Medicine, Southeast University, Nanjing, China.
Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China.
Front Public Health. 2022 May 17;10:914193. doi: 10.3389/fpubh.2022.914193. eCollection 2022.
RNA N6-methyladenosine (m6A) regulators may be necessary for diverse viral infectious diseases, and serve pivotal roles in various physiological functions. However, the potential roles of m6A regulators in coronavirus disease 2019 (COVID-19) remain unclear.
The gene expression profile of patients with or without COVID-19 was acquired from Gene Expression Omnibus (GEO) database, and bioinformatics analysis of differentially expressed genes was conducted. Random forest modal and nomogram were established to predict the occurrence of COVID-19. Afterward, the consensus clustering method was utilized to establish two different m6A subtypes, and associations between subtypes and immunity were explored.
Based on the transcriptional data from GSE157103, we observed that the m6A modification level was markedly enriched in the COVID-19 patients than those in the non-COVID-19 patients. And 18 essential m6A regulators were identified with differential analysis between patients with or without COVID-19. The random forest model was utilized to determine 8 optimal m6A regulators for predicting the emergence of COVID-19. We then established a nomogram based on these regulators, and its predictive reliability was validated by decision curve analysis. The consensus clustering algorithm was conducted to categorize COVID-19 patients into two m6A subtypes from the identified m6A regulators. The patients in cluster A were correlated with activated T-cell functions and may have a superior prognosis.
Collectively, m6A regulators may be involved in the prevalence of COVID-19 patients. Our exploration of m6A subtypes may benefit the development of subsequent treatment modalities for COVID-19.
RNA N6-甲基腺苷（m6A）调节剂可能是多种病毒感染性疾病所必需的，并且在各种生理功能中发挥关键作用。然而，m6A 调节剂在 2019 年冠状病毒病（COVID-19）中的潜在作用尚不清楚。
从基因表达综合数据库（GEO）数据库中获取 COVID-19 患者或无 COVID-19 患者的基因表达谱，并对差异表达基因进行生物信息学分析。建立随机森林模型和列线图来预测 COVID-19 的发生。然后，采用共识聚类方法建立两种不同的 m6A 亚型，并探讨亚型与免疫的关系。
基于 GSE157103 的转录数据，我们观察到 COVID-19 患者的 m6A 修饰水平明显高于非 COVID-19 患者。并且在 COVID-19 患者与非 COVID-19 患者之间的差异分析中鉴定出 18 个重要的 m6A 调节剂。利用随机森林模型确定了 8 个最佳 m6A 调节剂来预测 COVID-19 的发生。然后，我们基于这些调节剂建立了一个列线图，并通过决策曲线分析验证了其预测可靠性。利用共识聚类算法，根据鉴定出的 m6A 调节剂将 COVID-19 患者分为两种 m6A 亚型。聚类 A 的患者与激活的 T 细胞功能相关，可能具有更好的预后。
总之，m6A 调节剂可能参与了 COVID-19 患者的流行。我们对 m6A 亚型的探索可能有助于开发 COVID-19 的后续治疗方法。
