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基于累积的正常组织数据对先兆子痫中受干扰的通路和重要基因进行个性化发现。

Personalized discovery of disrupted pathways and significant genes in preeclampsia based on accumulated normal tissue data.

作者信息

Luo Ying, Ma Xiao-Chen, Gao Qing, Cao Lu-Quan

机构信息

Department of Prenatal Diagnosis, Jinan Maternity and Child Care Hospital, Jinan 250001, PR China.

出版信息

J Cancer Res Ther. 2018;14(7):1644-1649. doi: 10.4103/0973-1482.203603.

DOI:10.4103/0973-1482.203603
PMID:30589053
Abstract

PURPOSE

This study was designed to identify disrupted pathways in an individual with preeclampsia (PE) using accumulated normal sample data based on individualized pathway aberrance score (iPAS) method.

MATERIALS AND METHODS

Pathway data were obtained from the Reactome database. Next, the average Z algorithm was utilized to compute the iPAS. The disrupted pathways in a PE sample were identified by means of t test according to the pathway statistics values of normal and PE samples. In addition, we screened the differential expressed genes (DEGs) using SAMR package and constructed the differential co-expression network comprising DEGs. Subsequently, topological analysis for the co-expression network was conducted to identify hub genes.

RESULTS

Under the threshold of false discovery rate <0.05, 69 disrupted pathways were selected. Among them, formation of tubulin-folding intermediates by containing t-complex polypeptide 1 (CCT)/TCP1 ring complex (TriC) was the most remarkable pathway. Degree analysis for co-expression network of DEGs suggested that there were several hub-disrupted pathway-related genes, for instance, TCP1 and TUBA1A. More importantly, these two hub genes were enriched in the most significant pathway of formation of tubulin-folding intermediates by CCT/TriC.

CONCLUSION

The iPAS method is suitable for identifying disrupted pathways in PE. Pathway of formation of tubulin folding intermediates by CCT/TriC might play important roles in PE.

摘要

目的

本研究旨在基于个性化通路异常评分(iPAS)方法,利用积累的正常样本数据,识别子痫前期(PE)个体中受干扰的通路。

材料与方法

通路数据来自Reactome数据库。接下来,使用平均Z算法计算iPAS。根据正常样本和PE样本的通路统计值,通过t检验识别PE样本中受干扰的通路。此外,我们使用SAMR软件包筛选差异表达基因(DEG),并构建包含DEG的差异共表达网络。随后,对共表达网络进行拓扑分析以识别枢纽基因。

结果

在错误发现率<0.05的阈值下,选择了69条受干扰的通路。其中,由包含t-复合体多肽1(CCT)/TCP1环复合体(TriC)形成微管蛋白折叠中间体是最显著的通路。对DEG共表达网络的度分析表明,有几个与枢纽受干扰通路相关的基因,例如TCP1和TUBA1A。更重要的是,这两个枢纽基因在由CCT/TriC形成微管蛋白折叠中间体的最显著通路中富集。

结论

iPAS方法适用于识别PE中受干扰的通路。由CCT/TriC形成微管蛋白折叠中间体的通路可能在PE中起重要作用。

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