Kawasaki Kaoru, Kondoh Eiji, Chigusa Yoshitsugu, Ujita Mari, Murakami Ryusuke, Mogami Haruta, Brown J B, Okuno Yasushi, Konishi Ikuo
Department of Gynecology and Obstetrics, Kyoto University, Kyoto, Japan.
Department of Gynecology and Obstetrics, Kyoto University, Kyoto, Japan
Mol Hum Reprod. 2015 Feb;21(2):217-24. doi: 10.1093/molehr/gau096. Epub 2014 Oct 16.
Pre-eclampsia is a multifactorial disorder characterized by heterogeneous clinical manifestations. Gene expression profiling of preeclamptic placenta have provided different and even opposite results, partly due to data compromised by various experimental artefacts. Here we aimed to identify reliable pre-eclampsia-specific pathways using multiple independent microarray data sets. Gene expression data of control and preeclamptic placentas were obtained from Gene Expression Omnibus. Single-sample gene-set enrichment analysis was performed to generate gene-set activation scores of 9707 pathways obtained from the Molecular Signatures Database. Candidate pathways were identified by t-test-based screening using data sets, GSE10588, GSE14722 and GSE25906. Additionally, recursive feature elimination was applied to arrive at a further reduced set of pathways. To assess the validity of the pre-eclampsia pathways, a statistically-validated protocol was executed using five data sets including two independent other validation data sets, GSE30186, GSE44711. Quantitative real-time PCR was performed for genes in a panel of potential pre-eclampsia pathways using placentas of 20 women with normal or severe preeclamptic singleton pregnancies (n = 10, respectively). A panel of ten pathways were found to discriminate women with pre-eclampsia from controls with high accuracy. Among these were pathways not previously associated with pre-eclampsia, such as the GABA receptor pathway, as well as pathways that have already been linked to pre-eclampsia, such as the glutathione and CDKN1C pathways. mRNA expression of GABRA3 (GABA receptor pathway), GCLC and GCLM (glutathione metabolic pathway), and CDKN1C was significantly reduced in the preeclamptic placentas. In conclusion, ten accurate and reliable pre-eclampsia pathways were identified based on multiple independent microarray data sets. A pathway-based classification may be a worthwhile approach to elucidate the pathogenesis of pre-eclampsia.
子痫前期是一种具有异质性临床表现的多因素疾病。子痫前期胎盘的基因表达谱分析得出了不同甚至相反的结果,部分原因是数据受到各种实验假象的影响。在此,我们旨在使用多个独立的微阵列数据集来识别可靠的子痫前期特异性通路。对照胎盘和子痫前期胎盘的基因表达数据从基因表达综合数据库获取。进行单样本基因集富集分析,以生成从分子特征数据库获得的9707条通路的基因集激活分数。使用数据集GSE10588、GSE14722和GSE25906,通过基于t检验的筛选来识别候选通路。此外,应用递归特征消除以进一步减少通路集。为评估子痫前期通路的有效性,使用包括两个独立的其他验证数据集GSE30186、GSE44711在内的五个数据集执行了经过统计验证的方案。使用20名正常或重度子痫前期单胎妊娠女性(各n = 10)的胎盘,对一组潜在的子痫前期通路中的基因进行定量实时PCR。发现一组十条通路能够高精度地区分子痫前期女性和对照。其中包括先前与子痫前期无关的通路,如GABA受体通路,以及已经与子痫前期相关的通路,如谷胱甘肽和CDKN1C通路。子痫前期胎盘中GABRA3(GABA受体通路)、GCLC和GCLM(谷胱甘肽代谢通路)以及CDKN1C的mRNA表达显著降低。总之,基于多个独立的微阵列数据集识别出了十条准确可靠的子痫前期通路。基于通路的分类可能是阐明子痫前期发病机制的一种有价值的方法。