Wenderfer Scott E
BC Children's Hospital, Nephrology, Vancouver, BC, Canada.
The University of British Columbia, Faculty of Medicine, Department of Pediatrics, Vancouver, BC, Canada.
Pediatr Res. 2025 Aug 7. doi: 10.1038/s41390-025-04314-4.
Bioinformatics has the potential to provide new insights into the pathogenic mechanisms of complex childhood-onset diseases. Otoferlin is one example. There has been an upsurge in utilization of bioinformatics techniques to analyze large data sets in systemic lupus and other diseases. While this can result in large numbers of innovative new biomarkers, diagnostics, and targeted therapies, readers should be mindful of the limitations of bioinformatic analyses. In specific, limitations in the size and diversity of the cohorts or bio-specimens used to generate the original datasets may lead to bias. Moreover, the rigor of the data analysis plan is critical to reproducibility of the findings. Systemic lupus erythematous is an extremely heterogeneous syndrome. There are five classes of lupus nephritis and several other non-classifiable histologic forms of lupus kidney disease. There are at least 23 distinct cell types that comprise the kidney parenchyma, and dozens of other types of infiltrating bone-marrow derived cells. In pediatric research, interpretation of bioinformatics data requires an appreciation for age and developmental effects on anatomy and physiology. With informed methodologies, the repurposing of large data sets from pediatric patients for meta-analyses can advance our understanding of childhood illnesses like lupus nephritis. IMPACT: Integrative bioinformatics is a powerful approach to repurpose large data sets for discovery research. The manuscript by Wu et al. in Pediatric Research identifies otoferlin (OTOF) as a promising new diagnostic biomarker of childhood-onset lupus nephritis. Pediatric researchers should gain more appreciation for the available algorithms for analyzing large data sets from multi-omic projects and funding should be targeted for validating findings in childhood-onset diseases.
生物信息学有潜力为儿童期起病的复杂疾病的致病机制提供新的见解。耳铁蛋白就是一个例子。在系统性红斑狼疮和其他疾病中，利用生物信息学技术分析大数据集的情况激增。虽然这可能会产生大量创新的新生物标志物、诊断方法和靶向治疗方法，但读者应注意生物信息学分析的局限性。具体而言，用于生成原始数据集的队列或生物样本的规模和多样性方面的局限性可能会导致偏差。此外，数据分析计划的严谨性对于研究结果的可重复性至关重要。系统性红斑狼疮是一种极其异质性的综合征。有五类狼疮性肾炎以及几种其他不可分类的狼疮肾病组织学形式。构成肾实质的细胞类型至少有23种，还有数十种其他类型的浸润性骨髓衍生细胞。在儿科研究中，对生物信息学数据的解释需要了解年龄以及发育对解剖学和生理学的影响。采用明智的方法，将儿科患者的大数据集重新用于荟萃分析，可以增进我们对狼疮性肾炎等儿童疾病的理解。影响：整合生物信息学是一种将大数据集重新用于发现研究的强大方法。吴等人发表在《儿科研究》上的论文将耳铁蛋白（OTOF）确定为儿童期起病的狼疮性肾炎一种有前景的新诊断生物标志物。儿科研究人员应更深入了解用于分析来自多组学项目的大数据集的现有算法，并且应将资金用于验证儿童期起病疾病的研究结果。
