Department of Nephropathology, Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Krankenhausstr. 8-10, D-91054 Erlangen, Germany.
Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Krankenhausstr. 8-10, D-91054 Erlangen, Germany.
Biol Chem. 2021 May 5;402(8):953-972. doi: 10.1515/hsz-2020-0378. Print 2021 Jul 27.
In order to take advantage of the continuously increasing number of transcriptome studies, it is important to develop strategies that integrate multiple expression datasets addressing the same biological question to allow a robust analysis. Here, we propose a meta-analysis framework that integrates enriched pathways identified through the Gene Set Enrichment Analysis (GSEA) approach and calculates for each meta-pathway an empirical -value. Validation of our approach on benchmark datasets showed comparable or even better performance than existing methods and an increase in robustness with increasing number of integrated datasets. We then applied the meta-analysis framework to 15 functional genomics datasets of physiological and pathological cardiac hypertrophy. Within these datasets we grouped expression sets measured at time points that represent the same hallmarks of heart tissue remodeling ('aggregated time points') and performed meta-analysis on the expression sets assigned to each aggregated time point. To facilitate biological interpretation, results were visualized as gene set enrichment networks. Here, our meta-analysis framework identified well-known biological mechanisms associated with pathological cardiac hypertrophy (e.g., cardiomyocyte apoptosis, cardiac contractile dysfunction, and alteration in energy metabolism). In addition, results highlighted novel, potentially cardioprotective mechanisms in physiological cardiac hypertrophy involving the down-regulation of immune cell response, which are worth further investigation.
为了充分利用不断增加的转录组研究数量，开发整合多个表达数据集的策略至关重要，这些数据集应针对相同的生物学问题，以实现稳健的分析。在这里，我们提出了一种元分析框架，该框架整合了通过基因集富集分析（GSEA）方法识别的富集途径，并为每个元途径计算经验值。在基准数据集上验证我们的方法表明，其性能与现有方法相当，甚至更好，并且随着整合数据集数量的增加，稳健性也得到提高。然后，我们将元分析框架应用于 15 个生理和病理性心脏肥大的功能基因组学数据集。在这些数据集中，我们将在代表心脏组织重构相同标志的时间点测量的表达集进行分组（“聚合时间点”），并对分配给每个聚合时间点的表达集进行元分析。为了便于生物学解释，结果以基因集富集网络的形式可视化。在这里，我们的元分析框架确定了与病理性心脏肥大相关的众所周知的生物学机制（例如，心肌细胞凋亡、心脏收缩功能障碍和能量代谢改变）。此外，结果突出了生理心脏肥大中潜在的、有保护作用的新机制，涉及免疫细胞反应的下调，这值得进一步研究。
