Journal of inflammation research · 2026
Exploring Potential Stress Granule-Related Biomarkers in Childhood Asthma Using Integrated Bioinformatics and Machine Learning.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Zou Ya, Ren Hua, Zheng Jun, Huang Yanjie
Аннотация
OBJECTIVE: Asthma is a common chronic lung disease in children, but the role of stress granules (SGs) in its pathogenesis remains unclear. The present study aims to investigate overlapping genes and regulatory mechanisms between childhood asthma (CA) and SGs via integrated bioinformatics and machine learning.
METHODS: Machine learning algorithms were applied to screen for potential biomarkers among the candidate genes. Their expression levels were validated, followed by ROC analysis. Multiple bioinformatics analyses, including mRNA-miRNA and transcription factor regulatory networks, were performed to explore their potential functions. Finally, RT-qPCR was used to validate the expression differences between CA and healthy control blood samples.
RESULTS: Through machine learning, 11 candidate genes were initially selected, with 3 potential biomarkers (HNRNPA2B1, RPE, TAF15) determined. These biomarkers showed strong diagnostic performance (AUC > 0.7). GeneMANIA and Gene Set Enrichment Analysis (GSEA) revealed that their functions were enriched in biological processes such as NADPH regeneration. Immunoinfiltration analysis identified four types of differentially infiltrating immune cells: CD56dim natural killer cell, Central memory CD8 T cell, Immature B cell, and Monocyte. Additionally, RT-PCR validation confirmed significantly elevated mRNA expression of HNRNPA2B1, RPE, and TAF15 in CA patients compared to healthy controls, consistent with the bioinformatics predictions.
CONCLUSION: This study screened out three potential biomarkers related to SGs in CA, offering new insights into disease pathogenesis and potential molecular targets for improved therapy.
METHODS: Machine learning algorithms were applied to screen for potential biomarkers among the candidate genes. Their expression levels were validated, followed by ROC analysis. Multiple bioinformatics analyses, including mRNA-miRNA and transcription factor regulatory networks, were performed to explore their potential functions. Finally, RT-qPCR was used to validate the expression differences between CA and healthy control blood samples.
RESULTS: Through machine learning, 11 candidate genes were initially selected, with 3 potential biomarkers (HNRNPA2B1, RPE, TAF15) determined. These biomarkers showed strong diagnostic performance (AUC > 0.7). GeneMANIA and Gene Set Enrichment Analysis (GSEA) revealed that their functions were enriched in biological processes such as NADPH regeneration. Immunoinfiltration analysis identified four types of differentially infiltrating immune cells: CD56dim natural killer cell, Central memory CD8 T cell, Immature B cell, and Monocyte. Additionally, RT-PCR validation confirmed significantly elevated mRNA expression of HNRNPA2B1, RPE, and TAF15 in CA patients compared to healthy controls, consistent with the bioinformatics predictions.
CONCLUSION: This study screened out three potential biomarkers related to SGs in CA, offering new insights into disease pathogenesis and potential molecular targets for improved therapy.