Journal of investigational allergology & clinical immunology · 2026
Development of a Panel of Molecular Biomarkers of Asthma: Analysis of Differential Expression and Stability Over Time.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Cremades-Jimeno Lucía, López-Ramos María, Baos Selen, Fernández-Santamaría Rubén, de Pedro María A, Mahíllo-Fernández Ignacio, Rosales-Ariza Cristina, Olaguibel José M, Del Pozo Victoria, Caballero María L, Luna-Porta Juan A, Quirce Santiago, Barroso Blanca, Betancor Diana, Valverde-Monge Marcela, Sastre Joaquín, Cárdaba Blanca
Аннотация
BACKGROUND: Asthma is a chronic inflammatory respiratory disease characterized by significant heterogeneity, which complicates accurate patient classification and management. With the aim of defining new, reliable biomarkers, we previously evaluated the potential of 94 genes to differentiate allergic asthma (AA) from nonallergic asthma (NA) based on their expression in peripheral blood mononuclear cells (PBMCs). Here, the most promising biomarkers were further analyzed in a 2-year longitudinal cohort of 24 healthy controls (HCs), 18 NA patients, and 51 AA patients.
METHODS: PBMC samples were collected at the beginning of the study (T0) and 2 years later (T2). The expression of 26 genes was analyzed using RT-qPCR. Genes showing stable expression over time (ie, with a high correlation between T0 and T2) were selected for further analysis. Differential expression and receiver operating characteristic (ROC) curve analysis were used to identify the best biomarkers for discrimination between phenotypes.
RESULTS: Longitudinal stability was good for 13 genes. Of these, 11 showed statistically significant differential expression between asthma patients and HCs. ROC curve analyses were used to rank these genes by their discriminatory power. Specifically, CPA3 expression was able to discriminate asthma patients from HCs, while LGALS3 and TGFß1 made it possible to differentiate between NA and AA. Additionally, asthma severity was assessed based on the expression levels of RNASE3, IL4R, CHI3L1, PI3, and IL1R2.
CONCLUSIONS: We propose a diagnostic algorithm based on the expression profiles of 8 genes in PBMCs that could guide clinicians in the diagnosis and phenotypic classification of asthma.
METHODS: PBMC samples were collected at the beginning of the study (T0) and 2 years later (T2). The expression of 26 genes was analyzed using RT-qPCR. Genes showing stable expression over time (ie, with a high correlation between T0 and T2) were selected for further analysis. Differential expression and receiver operating characteristic (ROC) curve analysis were used to identify the best biomarkers for discrimination between phenotypes.
RESULTS: Longitudinal stability was good for 13 genes. Of these, 11 showed statistically significant differential expression between asthma patients and HCs. ROC curve analyses were used to rank these genes by their discriminatory power. Specifically, CPA3 expression was able to discriminate asthma patients from HCs, while LGALS3 and TGFß1 made it possible to differentiate between NA and AA. Additionally, asthma severity was assessed based on the expression levels of RNASE3, IL4R, CHI3L1, PI3, and IL1R2.
CONCLUSIONS: We propose a diagnostic algorithm based on the expression profiles of 8 genes in PBMCs that could guide clinicians in the diagnosis and phenotypic classification of asthma.