Construction of an miRNA-Regulated Pathway Network Reveals Candidate Biomarkers for Postmenopausal Osteoporosis.
Construction of an miRNA-Regulated Pathway Network Reveals Candidate Biomarkers for Postmenopausal Osteoporosis.
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miRNA 调控通路网络的构建揭示了绝经后骨质疏松症的候选生物标志物
DOI：
10.1155/2017/9426280
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发表时间：
2017
影响因子：
--
通讯作者：
Shao M
中科院分区：
文献类型：
--
作者：
Shao M
我们的目的是通过建立microRNAs-（miRNA-）调节通路网络（MRPN）来识别绝经后骨质疏松症（PMOP）的风险通路。首先，我们通过计算基因和通路水平的统计数据的基础上积累的正常样本使用个别通路畸变分数（iPAS）的差异途径。基于差异表达基因（DEG）使用大卫提取的重要途径，然后通过识别iPAS和大卫方法之间的共同途径。接下来，通过用预先计算的输入（对数倍数变化（FC）、TargetScan上下文得分（TSCS）和保守靶向的概率（PCT））计算TargetScore值来实施miRNA预测。使用共同途径中的共同基因和预测的miRNA构建MRPN构建体。使用假发现率（FDR）< 0.05，鉴定了279个差异通路。使用FDR < 0.05和|原木纤维|≥ 2，共检索到39个DEG，这些DEG富集在大卫鉴定的64个重要通路中。两种方法共有27条通路。MAPK信号通路和PI 3 K-Akt信号通路分别是第一和第二显著富集的信号通路。这27个共同途径将PMOP与对照分开，准确度为0.912。MAPK信号通路和PI 3 K/Akt信号通路可能在PMOP中起重要作用。
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 |logFC| ≥ 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.
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影响因子：
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作者：
Michaëlsson, K;Melhus, H;Pedersen, NL
通讯作者：
Pedersen, NL
影响因子：
0.6
作者：
Chen, Hua;Jiang, Honghui;Guo, Song
通讯作者：
Guo, Song
影响因子：
0.4
作者：
Song, J. F.;Jing, Z. Z.;Su, Y. X.
通讯作者：
Su, Y. X.
DOI：
10.1093/bioinformatics/btq675
发表时间：
2011-02-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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作者：
Smoot ME;Ono K;Ruscheinski J;Wang PL;Ideker T
通讯作者：
Ideker T
整合表观基因组元素和 GWAS 识别影响骨矿物质密度和骨质疏松性骨折风险的 BDNF 基因
影响因子：
4.6
作者：
Guo Y;Dong SS;Chen XF;Jing YA;Yang M;Yan H;Shen H;Chen XD;Tan LJ;Tian Q;Deng HW;Yang TL
通讯作者：
Yang TL