tetano
Editor, Senior Moderator
Virol J
. 2025 Aug 28;22(1):293.
doi: 10.1186/s12985-025-02660-7. From correlation to causation: unraveling the role of long non-coding RNAs in COVID-19 pathogenesis
Tianfei Yu[SUP] 1 2 [/SUP], Yunhan Zhang[SUP] 3 4 [/SUP], Haolan Zhang[SUP] 3 4 [/SUP], Ming Li[SUP] 5 [/SUP]
Affiliations
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.
Keywords: COVID-19 biomarkers; Diagnostic potential; Inflammation and fibrosis; Long non-coding RNAs (lncRNAs); Machine learning applications.
. 2025 Aug 28;22(1):293.
doi: 10.1186/s12985-025-02660-7. From correlation to causation: unraveling the role of long non-coding RNAs in COVID-19 pathogenesis
Tianfei Yu[SUP] 1 2 [/SUP], Yunhan Zhang[SUP] 3 4 [/SUP], Haolan Zhang[SUP] 3 4 [/SUP], Ming Li[SUP] 5 [/SUP]
Affiliations
- PMID: 40877945
- 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.
Keywords: COVID-19 biomarkers; Diagnostic potential; Inflammation and fibrosis; Long non-coding RNAs (lncRNAs); Machine learning applications.