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

Abstract

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. 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. 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. 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.