BACKGROUND: Asthma is a syndrome characterized by airway inflammation and obstruction. Due to its heterogeneity, the difficulties in asthma diagnosis and treatment make the discovery of new biomarkers a focus of research. So, we determined the differential miRNA expression of eosinophils between healthy and asthmatic patients and to establish a differentially expressed miRNA profile detectable in sera for use as biomarker. METHODS: MicroRNAs from peripheral eosinophils from healthy and asthmatic subjects were isolated and analyzed by next-generation sequencing and confirmed by quantitative PCR in 29 asthmatics and 10 healthy individuals. The levels of serum miRNAs were performed by quantitative PCR in 138 asthmatics and 39 healthy subjects. Regression analysis and Random Forest models were performed. RESULTS: We found a set of miRNAs whose expression differs between eosinophils from asthmatics and healthy subjects. These miRNAs can classify asthmatics into two clusters that differed in the number of eosinophils and periostin concentration in serum. Some of these miRNAs were also confirmed in sera, as miR-185-5p which discriminates asthmatics from healthy subjects. Together with other two miRNAs, miR-185-5p allowed us to create a logistic regression model to discriminate better both conditions and a Random Forest model that can even sort the asthmatics into intermittent, mild persistent, moderate persistent, and severe persistent asthma. CONCLUSION: Our data show that miRNAs profile in eosinophils can be used as asthma diagnosis biomarker in serum and that this profile is able to rank asthma severity.
BACKGROUND:Asthma is a syndrome characterized by airway inflammation and obstruction. Due to its heterogeneity, the difficulties in asthma diagnosis and treatment make the discovery of new biomarkers a focus of research. So, we determined the differential miRNA expression of eosinophils between healthy and asthmatic patients and to establish a differentially expressed miRNA profile detectable in sera for use as biomarker. METHODS: MicroRNAs from peripheral eosinophils from healthy and asthmatic subjects were isolated and analyzed by next-generation sequencing and confirmed by quantitative PCR in 29 asthmatics and 10 healthy individuals. The levels of serum miRNAs were performed by quantitative PCR in 138 asthmatics and 39 healthy subjects. Regression analysis and Random Forest models were performed. RESULTS: We found a set of miRNAs whose expression differs between eosinophils from asthmatics and healthy subjects. These miRNAs can classify asthmatics into two clusters that differed in the number of eosinophils and periostin concentration in serum. Some of these miRNAs were also confirmed in sera, as miR-185-5p which discriminates asthmatics from healthy subjects. Together with other two miRNAs, miR-185-5p allowed us to create a logistic regression model to discriminate better both conditions and a Random Forest model that can even sort the asthmatics into intermittent, mild persistent, moderate persistent, and severe persistent asthma. CONCLUSION: Our data show that miRNAs profile in eosinophils can be used as asthma diagnosis biomarker in serum and that this profile is able to rank asthma severity.
Authors: José M Rodrigo-Muñoz; Marta Gil-Martínez; Clara Lorente-Sorolla; Raquel García-Latorre; Marcela Valverde-Monge; Santiago Quirce; Joaquín Sastre; Victoria Del Pozo Journal: Front Immunol Date: 2022-04-01 Impact factor: 8.786
Authors: José M Rodrigo-Muñoz; José A Cañas; Beatriz Sastre; Marta Gil-Martínez; Raquel García Latorre; Joaquín Sastre; Victoria Del Pozo Journal: J Cell Physiol Date: 2021-10-26 Impact factor: 6.513