Liu Ji-Long, Zhao Miao
College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China.
Int J Mol Sci. 2016 Feb 1;17(2):191. doi: 10.3390/ijms17020191.
Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%-2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk-based computational method named TM-rank to prioritize ectopic pregnancy-related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes.
异位妊娠是一种非常危险的妊娠并发症，在所有报告的妊娠中占1%-2%。由于人体活检存在伦理限制以及缺乏合适的动物模型，在确定异位妊娠发病机制中功能重要基因方面进展甚微。在本研究中，我们开发了一种基于随机游走的计算方法，名为TM-rank，用于根据文本挖掘数据和基因网络信息对异位妊娠相关基因进行优先级排序。使用定义的阈值，我们确定了五个排名靠前的基因：VEGFA（血管内皮生长因子A）、IL8（白细胞介素8）、IL6（白细胞介素6）、ESR1（雌激素受体1）和EGFR（表皮生长因子受体）。这些基因是很有前景的候选基因，可作为有用的诊断生物标志物和治疗靶点。我们的方法代表了一种对疾病易感基因进行优先级排序的新策略。
