Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm

Abstract

This study investigated optimal pathways for preeclampsia (PE) utilizing the network-based guilt by association (GBA) algorithm. The inference method consisted of four steps: preparing differentially expressed genes (DEGs) between PE patients and normal controls from gene expression data; constructing co-expression network (CEN) for DEGs utilizing Spearman's correlation coefficient (SCC) method; and predicting optimal pathways by network-based GBA algorithm of which the area under the receiver operating characteristics curve (AUROC) was gained for each pathway. There were 351 DEGs and 61,425 edges in the CEN for PE. Subsequently, 53 pathways were obtained with a good classification performance (AUROC >0.5). AUROC for 9 was >0.9 and defined as optimal pathways, especially microRNAs in cancer (AUROC=0.9966), gap junction (AUROC=0.9922), and pathogenic Escherichia coli infection (AUROC=0.9888). Nine optimal pathways were identified through comprehensive analysis of data from PE patients, which might shed new light on uncovering molecular and pathological mechanism of PE.

Keywords: co-expression network; guilt by association; pathway; preeclampsia.

Figure 1.

The AUROC distribution among GO terms. AUROC for large amount of pathways distributed to the section of 0.4–0.6 and 0.75–0.9.