Prioritization of Susceptibility Genes for Ectopic Pregnancy by Gene Network Analysis

Abstract

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.

Keywords: ectopic pregnancy; gene prioritization; pathogenesis; text mining.

Figure 1

Systematic identification of susceptibility genes for ectopic pregnancy. (A) Overview of the experimental design; (B) Cumulative number of publications related to ectopic pregnancy by year (from 1980 to 2015); (C) Distribution of the number of publications per gene.

Figure 2

The gene network underlying ectopic pregnancy susceptibility genes. (A) The structure of the gene network generated by the STRING software. The number of publications for each gene was represented in color gradient with a scale bar; (B) Degree distribution of the gene network. The degree distribution follows a power law distribution. With 95% confidence interval, the scaling exponent is estimated to be 0.9614 ± 0.1346.

Figure 3

Prioritization of susceptibility genes for ectopic pregnancy by the TM-rank algorithm. (A) Convergence of the TM-rank algorithm. Convergence was confirmed by observing an exponential decrease in the RMSD (root mean square deviation) values during the sequential iteration; (B) Gene ranking by the TM-rank algorithm. Genes were sorted according to rank values in descending order. Top-ranked genes, which exceeded the mean plus two standard deviations, were labeled.

Figure 4

Pathway analysis. (A) Pathway assignment for the top five genes recommended by the TM-rank algorithm. This graph was generated by using the Cytoscape software. Ellipse nodes in red represent genes and rectangle nodes in blue represent pathways; (B) Pathway enrichment analysis for all the 264 genes identified by text mining. This analysis was performed by using the DAVID tool. The significance cutoff for FDR was set at 0.01.