Center of Genomics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA.
Department of Cell and Molecular Biology, Tulane University, New Orleans, LA, 70118, USA.
Sci Rep. 2018 Jan 12;8(1):668. doi: 10.1038/s41598-018-19196-2.
Osteoporosis is a prevalent bone metabolic disease and peripheral blood monocytes represent a major systemic cell type for bone metabolism. To identify the key dysfunctional pathways in osteoporosis, we performed pathway analyses on microarray data of monocytes from subjects with extremely high/low hip bone mineral density. We first performed a traditional pathway analysis for which different pathways were treated as independent. However, genes overlap among pathways will lead to "crosstalk" phenomenon, which may lead to false positive/negative results. Therefore, we applied correction techniques including a novel approach that considers the correlation among genes to adjust the crosstalk effects in the analysis. In traditional analysis, 10 pathways were found to be significantly associated with BMD variation. After correction for crosstalk effects, three of them remained significant. Moreover, the MAPK signaling pathway, which has been shown to be important for osteoclastogenesis, became significant only after the correction for crosstalk effects. We also identified a new module mainly consisting of genes present in mitochondria to be significant. In summary, we describe a novel method to correct the crosstalk effect in pathway analysis and found five key independent pathways involved in BMD regulation, which may provide a better understanding of biological functional networks in osteoporosis.
骨质疏松症是一种常见的骨骼代谢疾病，外周血单核细胞代表了骨骼代谢的主要全身细胞类型。为了确定骨质疏松症中关键的功能失调途径，我们对来自髋骨骨密度极高/极低的受试者的单核细胞的微阵列数据进行了途径分析。我们首先进行了传统的途径分析，其中不同的途径被视为独立的。然而，途径之间的基因重叠会导致“串扰”现象，这可能导致假阳性/阴性结果。因此，我们应用了校正技术，包括一种考虑基因相关性的新方法，以调整分析中的串扰效应。在传统分析中，发现 10 条途径与 BMD 变化显著相关。校正串扰效应后，其中 3 条仍然显著。此外，MAPK 信号通路已被证明对破骨细胞生成很重要，只有在校正串扰效应后才变得显著。我们还鉴定了一个主要由线粒体中存在的基因组成的新模块是显著的。总之，我们描述了一种新的方法来校正途径分析中的串扰效应，并发现了五个参与 BMD 调节的关键独立途径，这可能为骨质疏松症中的生物学功能网络提供更好的理解。
