Interplay of LncRNAs NEAT1 and TUG1 in Incidence of Cytokine Storm in Appraisal of COVID-19 Infection

LncRNA NEAT1 和 TUG1 在评估 COVID-19 感染中对细胞因子风暴发生率的相互作用

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作者:Safaa I Tayel, Eman A El-Masry, Gehan A Abdelaal, Somaia Shehab-Eldeen, Abdallah Essa, Nashwa M Muharram

Background

In 2019, the coronavirus pandemic emerged, resulting in the highest mortality and morbidity rate globally. It has a prevailing transmission rate and continues to be a global burden. There is a paucity of data regarding the role of long non-coding RNAs (lncRNAs) in COVID-19. Therefore, the current study aimed to investigate lncRNAs, particularly NEAT1 and TUG1, and their association with IL-6, CCL2, and TNF-α in COVID-19 patients with moderate and severe disease.

Conclusion

In COVID-19 patients, significant overexpression of NEAT1 and TUG1 was observed, consistent with cytokine storm. TUG1 could be an efficient diagnostic biomarker, whereas NEAT1 was an independent predictor for overall survival.

Methods

The study was conducted on 80 COVID-19 patients (35 with severe and 45 with moderate infection) and 40 control subjects. Complete blood count (CBC), D-dimer assay, serum ferritin, and CRP were assayed. qRT-PCR was used to measure RNAs and lncRNAs.

Results

NEAT1 and TUG1 expression levels were higher in COVID-19 patients compared with controls (P<0.001). Furthermore, CCL2, IL-6, and TNF-α expressions were higher in COVID-19 patients compared to controls (P<0.001). CCL2 and IL-6 expression levels were significantly higher in patients with severe compared to those with moderate COVID-19 infection (P<0.001). IL-6 had the highest accuracy in distinguishing COVID-19 patients (AUC=1, P<0.001 at a cutoff of 0.359), followed by TUG1 (AUC=0.999, P<0.001 at a cutoff of 2.28). NEAT1 and TUG1 had significant correlations with the measured cytokines, and based on the multivariate regression analysis, NEAT1 is the independent predictor for survival in COVID-19 patients (P=0.02).

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