Meta-analysis of gene expression profiles in preeclampsia

Abstract Objective Preeclampsia is a serious complication of pregnancy. It is considered a complex condition influenced by maternal genes, environmental factors and a deregulated immune response of the mother, but the etiology is largely unknown. The aim of this study is to identify differentially expressed genes (DEGs) in preeclampsia, to help elucidate the identification of the disease etiological mechanisms. Study design The databases Pubmed and GEO were searched according to PRISMA guidelines for the existence of gene expression data on placental samples from case-control studies. After meta-analysis the identified DEGs were further analyzed with STRING and PANTHER to retrieve interaction networks and overrepresented biochemical pathways. Results Only 10 gene expression data-sets and articles fulfilled inclusion criteria, containing data on 195 patients and 231 controls, and were analyzed. Meta-analysis indentified 629 DEGs to be associated with preeclampsia at a False Discovery Rate p-value of 0.01. Network analysis showed few highly interconnected genes involved in innate immunity and signal transduction pathways indicative of a multifaceted disease with etiological heterogeneity. Over-representation analysis revealed that these genes participate mainly in carbohydrates, amino acids and pyrimidine metabolism, circadian clock system and signal transduction pathways. Conclusions This work, combining rigorous methods of meta-analysis and the use of modern bioinformatics tools, proposes the existence of novel, overlooked so far, biochemical pathways and mechanisms to contribute to preeclampsia development such as carbohydrate, aminoacids and pyrimidine metabolism. Our findings pave the way for further investigation of the above pathways in experimental efforts to decipher the orchestrating mechanisms for preeclampsia development.