Identification of novel biomarkers for preeclampsia on the basis of differential expression network analysis

Wu,Yufang; Fu,Xiuhua; Wang,Lin

doi:10.3892/etm.2016.3261

Identification of novel biomarkers for preeclampsia on the basis of differential expression network analysis

Authors:
- Yufang Wu
- Xiuhua Fu
- Lin Wang
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Affiliations: Department of Gynecology, Binzhou People's Hospital, Binzhou, Shandong 256610, P.R. China, Department of Obstetrics, Binzhou People's Hospital, Binzhou, Shandong 256610, P.R. China, Department of Obstetrics and Gynecology, Xijing Hospital, The First Affiliated Hospital of The Fourth Military Medical University, Xi'an, Shanxi 710032, P.R. China
Published online on: April 15, 2016 https://doi.org/10.3892/etm.2016.3261
Pages: 201-207
Copyright: © Wu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Preeclampsia (PE) is a severe pregnancy complication, which is a leading cause of maternal and fetal mortality. The present study aimed to screen potential biomarkers for the diagnosis and prediction of PE and to investigate the underlying mechanisms of PE development based on the differential expression network (DEN). The microarray datasets E‑GEOD‑6573 and E‑GEOD‑48424 were downloaded from the European Bioinformatics Institute database. Differentially expressed genes (DEGs) between the PE and normal groups were screened by Significant Analysis of Microarrays with the cutoff value of a |log2 fold change| of >2, and a false discovery rate of <0.05. The DEN was constructed based on the differential and non‑differential interactions observed. In addition, genes with higher connectivity degrees in the DEN were identified on the basis of centrality analysis, while disease genes were also extracted from the DEN. In order to understand the functional roles of genes in DEN, Gene Ontology (GO) and pathway enrichment analyses were performed. The present results indicated that a total of 225 genes were considered as DEGs in the PE group, while 466 nodes and 314 gene interactions were involved in the DEN. Among these 466 nodes, 4 nodes with higher degrees were identified, including ubiquitin C (UBC), small ubiquitin‑like modifier 1 (SUMO1), SUMO2 and RAD21 homolog (S. pombe) (RAD21). Notably, UBC was also found to be a disease gene. UBC, RAD21, SUMO2 and SUMO1 were markedly enriched in the regulation of programmed cell death, as well as in the regulation of apoptosis, cell cycle and chromosomal part. In conclusion, based on these results, we suggest that UBC, RAD21, SUMO2 and SUMO1 may be reliable biomarkers for the prediction of the development and progression of PE.

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