Yu Tianfei, Zhang Yunhan, Zhang Haolan, Li Ming
Department of Biotechnology, College of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, 161006, China.
Heilongjiang Provincial Key Laboratory of Resistance Gene Engineering and Protection of Biodiversity in Cold Areas, Qiqihar University, Qiqihar, 161006, China.
Virol J. 2025 Aug 28;22(1):293. doi: 10.1186/s12985-025-02660-7.
Heydari et al. present an intriguing study examining the role of three long non-coding RNAs (lncRNAs)-H19, taurine upregulated gene 1 (TUG1), and colorectal neoplasia differentially expressed (CRNDE)-in the context of Coronavirus Disease 2019 (COVID-19), focusing on their diagnostic potential and biological significance. The authors argue that these lncRNAs play a role in inflammatory and fibrotic processes associated with COVID-19 and demonstrate their potential utility as biomarkers using machine learning-based predictive models. While the study offers significant contributions to the field, there are limitations in its methodology, interpretative depth, and generalizability that merit closer examination. This commentary critically evaluates the findings, suggesting avenues for refinement and further research.
海达里等人发表了一项引人入胜的研究，该研究在2019年冠状病毒病（COVID-19）的背景下，探讨了三种长链非编码RNA（lncRNA）——H19、牛磺酸上调基因1（TUG1）和结直肠癌差异表达基因（CRNDE）的作用，重点关注它们的诊断潜力和生物学意义。作者认为，这些lncRNA在与COVID-19相关的炎症和纤维化过程中发挥作用，并使用基于机器学习的预测模型证明了它们作为生物标志物的潜在效用。虽然该研究为该领域做出了重大贡献，但其方法、解释深度和普遍性存在局限性，值得进一步审视。本评论对这些发现进行了批判性评估，提出了改进和进一步研究的途径。
