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Construction of an miRNA-Regulated Pathway Network Reveals Candidate Biomarkers for Postmenopausal Osteoporosis

机译:miRNA调控的通路网络的建设揭示了绝经后骨质疏松症的候选生物标志物。

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摘要

We aimed to identify risk pathways for postmenopausal osteoporosis (PMOP) via establishing an microRNAs- (miRNA-) regulated pathway network (MRPN). Firstly, we identified differential pathways through calculating gene- and pathway-level statistics based on the accumulated normal samples using the individual pathway aberrance score (iPAS). Significant pathways based on differentially expressed genes (DEGs) using DAVID were extracted, followed by identifying the common pathways between iPAS and DAVID methods. Next, miRNAs prediction was implemented via calculating TargetScore values with precomputed input (log fold change (FC), TargetScan context score (TSCS), and probabilities of conserved targeting (PCT)). An MRPN construction was constructed using the common genes in the common pathways and the predicted miRNAs. Using false discovery rate (FDR) < 0.05, 279 differential pathways were identified. Using the criteria of FDR < 0.05 and |log⁡FC| ≥ 2, 39 DEGs were retrieved, and these DEGs were enriched in 64 significant pathways identified by DAVID. Overall, 27 pathways were the common ones between two methods. Importantly, MAPK signaling pathway and PI3K-Akt signaling pathway were the first and second significantly enriched ones, respectively. These 27 common pathways separated PMOP from controls with the accuracy of 0.912. MAPK signaling pathway and PI3K/Akt signaling pathway might play crucial roles in PMOP.
机译:我们旨在通过建立microRNAs(miRNA)调控的通路网络(MRPN)来识别绝经后骨质疏松症(PMOP)的风险通路。首先,我们通过使用个体途径异常评分(iPAS),基于累积的正常样本计算基因和途径水平的统计数据,确定了差异途径。提取基于使用DAVID的差异表达基因(DEG)的重要途径,然后鉴定iPAS和DAVID方法之间的常见途径。接下来,通过使用预先计算的输入(对数倍变化(FC),TargetScan上下文得分(TSCS)和保守靶向的概率(PCT))计算TargetScore值来实现miRNA预测。使用共同途径中的共同基因和预测的miRNA构建MRPN构建。使用错误发现率(FDR)<0.05,鉴定了279条差异途径。使用FDR <0.05和|log⁡FC|的标准≥2,检索到39个DEG,这些DEG丰富了DAVID鉴定的64条重要途径。总体而言,两种方法之间共有27条途径。重要的是,MAPK信号通路和PI3K-Akt信号通路分别是第一个和第二个显着富集的通路。这27条常见途径将PMOP与对照组分开,准确度为0.912。 MAPK信号通路和PI3K / Akt信号通路可能在PMOP中起关键作用。

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