Coexpression and expression quantitative trait loci analyses of the angiogenesis gene-gene interaction network in prostate cancer.
Coexpression and expression quantitative trait loci analyses of the angiogenesis gene-gene interaction network in prostate cancer.
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DOI：
10.21037/tcr.2016.10.55
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发表时间：
2016-10
影响因子：
0.9
通讯作者：
Park JY
中科院分区：
文献类型：
--
作者：
Lin HY;Cheng CH;Chen DT;Chen YA;Park JY
前列腺癌(PCa)表现出明显的临床异质性。现有的基于临床因素的PCa预后风险分类方法是不够的。尽管已经确定了一些反映前列腺癌侵袭性的生物标志物，但其潜在的功能机制仍不清楚。我们之前报道了一个与PCa侵袭性相关的基因-基因相互作用网络，该网络基于血管生成途径中的单核苷酸多态(SNP)-SNP相互作用。这项研究的目的是调查潜在的功能证据，证明基因参与了这个基因-基因相互作用网络。共有11个血管生成基因被评估。通过共表达和表达数量性状基因座(EQTL)分析检测基因间的串扰。研究人群是癌症基因组图谱(TCGA)研究中的352名高加索前列腺癌患者。使用Spearman系数评估目的基因之间的成对共表达。EQTL分析采用Kruskal-Wallis检验。在所有基因内和55个可能的成对基因评估中，12对基因和1个基因(MMP16)显示出强烈的共表达或显著的eQTL证据。有9对基因具有很强的相关性(Spearman相关≥0.6，P<；1×10−13)。共表达基因最多的是EGFR-SP1(r=0.73)、ITGB3-HSPG2(r=0.71)、ITGB3-CSF1(r=0.70)、MMP16-FBLN5(r=0.68)、ITGB3-MMP16(r=0.65)、ITGB3-Robo1(r=0.62)、CSF1-HSPG2(r=0.61)、CSF1-FBLN5(r=0.6)和CSF1-Robo1(r=0.60)。MMP16中有1个顺式eQTL和5个反式eQTL(MMP16-ESR1、ESR1-Robo1、CSF1-Robo1、HSPG2-Robo1和FBLN5-CSF1)显著，假发现率Q值小于0.2。这些发现为这个血管生成网络中的基因-基因相互作用提供了潜在的生物学证据。这些已识别的血管生成基因之间的相互作用不仅为PCa的发病机制提供了信息，而且可能成为构建PCa侵袭性风险预测模型的综合生物标志物。
Prostate cancer (PCa) shows a substantial clinical heterogeneity. The existing risk classification for PCa prognosis based on clinical factors is not sufficient. Although some biomarkers for PCa aggressiveness have been identified, their underlying functional mechanisms are still unclear. We previously reported a gene-gene interaction network associated with PCa aggressiveness based on single nucleotide polymorphism (SNP)-SNP interactions in the angiogenesis pathway. The goal of this study is to investigate potential functional evidence of the involvement of the genes in this gene-gene interaction network. A total of 11 angiogenesis genes were evaluated. The crosstalks among genes were examined through coexpression and expression quantitative trait loci (eQTL) analyses. The study population is 352 Caucasian PCa patients in the Cancer Genome Atlas (TCGA) study. The pairwise coexpressions among the genes of interest were evaluated using the Spearman coefficient. The eQTL analyses were tested using the Kruskal-Wallis test. Among all within gene and 55 possible pairwise gene evaluations, 12 gene pairs and one gene (MMP16) showed strong coexpression or significant eQTL evidence. There are nine gene pairs with a strong correlation (Spearman correlation ≥0.6, P<1×10−13). The top coexpressed gene pairs are EGFR-SP1 (r=0.73), ITGB3-HSPG2 (r=0.71), ITGB3-CSF1 (r=0.70), MMP16-FBLN5 (r=0.68), ITGB3-MMP16 (r=0.65), ITGB3-ROBO1 (r=0.62), CSF1-HSPG2 (r=0.61), CSF1-FBLN5 (r=0.6), and CSF1-ROBO1 (r=0.60). One cis-eQTL in MMP16 and five trans-eQTLs (MMP16-ESR1, ESR1-ROBO1, CSF1-ROBO1, HSPG2-ROBO1, and FBLN5-CSF1) are significant with a false discovery rate q value less than 0.2. These findings provide potential biological evidence for the gene-gene interactions in this angiogenesis network. These identified interactions between the angiogenesis genes not only provide information for PCa etiology mechanism but also may serve as integrated biomarkers for building a risk prediction model for PCa aggressiveness.