The Journal of allergy and clinical immunology · 2025
Identification of potential transcriptomic and microRNA markers to assess benralizumab effectiveness.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Hirai Keita, Shirai Toshihiro, Uehara Sekiko, Akamatsu Taisuke, Itoh Kunihiko
Аннотация
BACKGROUND: Benralizumab targets IL-5 receptors and eliminates eosinophils, demonstrating its efficacy in type 2 inflammation-predominant asthma. Real-world evidence indicates variability in patient responses, emphasizing the need to understand the pathologic characteristics that influence its effectiveness.
OBJECTIVE: We postulated that patients with high and low benralizumab response would exhibit distinct pathophysiologic characteristics. We aimed to identify specific genes and microRNAs (miRNAs) as potential indicators of effectiveness-related characteristics.
METHODS: This prospective observational study included 17 patients with severe asthma undergoing benralizumab treatment. T-cell transcriptomic analysis was conducted before and 24 weeks after treatment initiation. To identify the genes associated with treatment efficacy, we identified differentially expressed genes (DEGs) and used a weighted gene coexpression network analysis. Additionally, serum miRNAs were analyzed quantitatively by using PCR arrays, and their correlation with gene clusters associated with treatment efficacy was investigated.
RESULTS: Transcriptomic analysis revealed that 223 DEGs were included in the response to benralizumab treatment. Weighted gene coexpression network analysis identified 21 coexpressed gene clusters, with most DEGs enriched in the 2 modules. One of these modules, the magenta module, was associated with the effectiveness of benralizumab treatment. Serum miRNA profiling identified 5 miRNAs associated with both treatment effectiveness and the magenta module (miR-7-5p, miR-155-5p, miR-320b, miR-342-3p, and miR-484). Among these, miR-7-5p showed the highest predictive accuracy for benralizumab effectiveness.
CONCLUSION: This study recognized specific gene expression patterns and serum miRNAs associated with the effectiveness of benralizumab in treating severe asthma. These findings contribute to our understanding of the asthma endotypes and may improve patient selection for benralizumab treatment.
OBJECTIVE: We postulated that patients with high and low benralizumab response would exhibit distinct pathophysiologic characteristics. We aimed to identify specific genes and microRNAs (miRNAs) as potential indicators of effectiveness-related characteristics.
METHODS: This prospective observational study included 17 patients with severe asthma undergoing benralizumab treatment. T-cell transcriptomic analysis was conducted before and 24 weeks after treatment initiation. To identify the genes associated with treatment efficacy, we identified differentially expressed genes (DEGs) and used a weighted gene coexpression network analysis. Additionally, serum miRNAs were analyzed quantitatively by using PCR arrays, and their correlation with gene clusters associated with treatment efficacy was investigated.
RESULTS: Transcriptomic analysis revealed that 223 DEGs were included in the response to benralizumab treatment. Weighted gene coexpression network analysis identified 21 coexpressed gene clusters, with most DEGs enriched in the 2 modules. One of these modules, the magenta module, was associated with the effectiveness of benralizumab treatment. Serum miRNA profiling identified 5 miRNAs associated with both treatment effectiveness and the magenta module (miR-7-5p, miR-155-5p, miR-320b, miR-342-3p, and miR-484). Among these, miR-7-5p showed the highest predictive accuracy for benralizumab effectiveness.
CONCLUSION: This study recognized specific gene expression patterns and serum miRNAs associated with the effectiveness of benralizumab in treating severe asthma. These findings contribute to our understanding of the asthma endotypes and may improve patient selection for benralizumab treatment.