Discovery of gene module acting on ubiquitin-mediated proteolysis pathway by co-expression network analysis for endometriosis

Abstract

Research question: Is abnormal gene module expression in the eutopic endometrium related to the occurrence of endometriosis?

Design: Nine datasets of normal and eutopic endometrium were searched and collected through the National Center for Biotechnology Information Gene Expression Omnibus, which included genome-wide expression studies of 71 normal cases and 142 endometriosis cases. Surrogate variable analysis was used for dataset integration. The network module and hub genes were selected by weighted gene co-expression network analysis. Machine learning was used to establish a diagnostic model of endometriosis.

Results: A gene module that was most relevant to endometriosis was selected through weighted gene co-expression network analysis. After further analysis of this module, four hub genes that represent the function of this module were selected: SCAF11, KRAS, MDM2 and KIF3A. Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the four hub genes revealed that all of them were most highly correlated with genes enriched in the ubiquitin-mediated proteolysis pathway. Moreover, in the correlation analysis between hub genes and Jab1, SCAF11 was found to be closely related to Jab1. Furthermore, hub genes were effective indicators for clinical diagnosis. The deep machine learning diagnostic model based on hub genes was highly sensitive.

Conclusions: The gene module identified is highly correlated with endometriosis. The four hub genes in this module degrade p27^kip1 through the ubiquitin-mediated proteolysis pathway to regulate the endometrium cell cycle and affect the development of endometriosis. The hub genes and the deep learning model based on them are valuable for clinical diagnosis.

Keywords: Diagnosis; Endometriosis; Hub genes; Machine learning; Ubiquitin-mediated proteolysis; Weighted gene co-expression network analysis.