Association of stem cell factor gene expression with severity and atopic state in patients with bronchial asthma.

BACKGROUND: Bronchial asthma is a chronic inflammatory and remodeling disorder of the airways, in which many cells, cellular elements, and cytokines play important roles. Stem cell factor (SCF) may contribute to the inflammatory changes occurring in asthma. We aimed to show the expression of SCF gene in patients with asthma as a means of diagnosis and its association with severity and atopic state in these patients. METHODS: This study was carried out on 80 subjects, 50 asthmatic patients and 30 age and gender matched healthy control persons. They were subjected to full history taking, general and local chest examination, spirometric measurements (pre and post broncodilators) using a spirometer, serum IgE, and real time PCR for assessment of SCF mRNA expression. RESULTS: This study showed significant difference between the studied groups regarding pulmonary function tests (P < 0.001). Asthmatic patients had significant higher SCF expression compared to control (P < 0.001), also atopic patients vs non atopic (P = 0.03) and severe asthmatic patients vs mild ones (P < 0.001). SCF expression at cut off point (0.528) is sufficient to discriminate asthmatic patients from control while at cut off point (1.84) for discrimination of atopic patients from non-atopic patients and at cut off point (1.395) for discrimination of severe asthmatic patients from mild ones. A significant negative correlation between SCF expression and inhaled steroid while significant positive correlation with serum IgE was found. CONCLUSION: Measuring SCF mRNA expression can be used as an efficient marker for evaluation of atopy and detection of severity of bronchial asthma.

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

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. 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). 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.