Rheumatology (Oxford, England) · 2026
Transcriptomic profiling of secukinumab-treated psoriatic arthritis reveals potential novel response-associated pathways.
СтатьяНаблюдательное исследованиеНазвание 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.
Abdul Jalil Nahdia Afiifah, Chinoy Hector, Jani Meghna, Barton Anne, Thompson Charlotte, Martin Paul, Nair Nisha, Bluett James, Outcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS)
Аннотация
OBJECTIVES: IL-17A inhibitors are therapeutic options in PsA, but response is not universal. Evidence from other inflammatory arthritides suggests differential gene expression may predict the outcomes. This study aimed to identify transcriptomic predictive biomarkers of response in PsA patients commencing secukinumab.
METHODS: Participants were recruited to OUtcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS), a prospective observational cohort study of patients with PsA initiating advanced therapeutics. Samples for this analysis were chosen based on extreme phenotype response. Whole-blood RNA sequencing was performed longitudinally in 13 secukinumab-treated patients at baseline (pre-treatment) and at 3 months post-treatment, with response evaluated at 3 months using the DAS28 criteria and the Psoriatic Arthritis Response Criteria. Differential gene expression analysis, Ingenuity Pathway Analysis (IPA), and weighted gene co-expression network analysis (WGCNA) were performed to identify significantly differentially expressed genes (DEGs) (adjusted P < 0.05, |log2 fold change| ≥ 1), enriched pathways (P < 0.05), and co-expressed gene modules. Immune cell subset proportions were estimated by deconvolution, and hub genes were identified by integrating DEGs and WGCNA, with overlapping genes defined as potential driver genes.
RESULTS: IGHV3-64D and IGHV1-46 were differentially expressed at baseline, 3 months, and sustained over time in the responder group (adjusted P < 0.05). Five overlapping genes (GMPR, CDC34, DMTN, UBXN6 and SLC25A39) were identified as potential drivers. Functional analysis indicated a potential contribution of metabolic pathways to the modulation of therapeutic response.
CONCLUSION: We identified two genes as pre-treatment predictive biomarkers of secukinumab response that persisted over time. Integration with WGCNA revealed five additional candidate genes. These genes are implicated in metabolic pathways, which may modulate the secukinumab response. These findings warrant further validation.
METHODS: Participants were recruited to OUtcomes of Treatment in Psoriatic Arthritis Study Syndicate (OUTPASS), a prospective observational cohort study of patients with PsA initiating advanced therapeutics. Samples for this analysis were chosen based on extreme phenotype response. Whole-blood RNA sequencing was performed longitudinally in 13 secukinumab-treated patients at baseline (pre-treatment) and at 3 months post-treatment, with response evaluated at 3 months using the DAS28 criteria and the Psoriatic Arthritis Response Criteria. Differential gene expression analysis, Ingenuity Pathway Analysis (IPA), and weighted gene co-expression network analysis (WGCNA) were performed to identify significantly differentially expressed genes (DEGs) (adjusted P < 0.05, |log2 fold change| ≥ 1), enriched pathways (P < 0.05), and co-expressed gene modules. Immune cell subset proportions were estimated by deconvolution, and hub genes were identified by integrating DEGs and WGCNA, with overlapping genes defined as potential driver genes.
RESULTS: IGHV3-64D and IGHV1-46 were differentially expressed at baseline, 3 months, and sustained over time in the responder group (adjusted P < 0.05). Five overlapping genes (GMPR, CDC34, DMTN, UBXN6 and SLC25A39) were identified as potential drivers. Functional analysis indicated a potential contribution of metabolic pathways to the modulation of therapeutic response.
CONCLUSION: We identified two genes as pre-treatment predictive biomarkers of secukinumab response that persisted over time. Integration with WGCNA revealed five additional candidate genes. These genes are implicated in metabolic pathways, which may modulate the secukinumab response. These findings warrant further validation.