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

J Cancer Res Ther. 2018;14(7):1644-1649. doi: 10.4103/0973-1482.203603.

Abstract

Purpose: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Differentially expressed genes; disrupted pathways; hub genes; individualized pathway aberrance score; preeclampsia.

MeSH terms

  • Algorithms
  • Computational Biology / methods
  • Databases, Genetic
  • Female
  • Gene Expression Profiling / methods
  • Gene Expression Regulation*
  • Gene Regulatory Networks*
  • Humans
  • Organ Specificity / genetics
  • Pre-Eclampsia / genetics*
  • Pre-Eclampsia / metabolism*
  • Precision Medicine
  • Pregnancy
  • Signal Transduction*