Identification of key transcription factors in endometrial cancer by systems bioinformatics analysis.

Abstract

Endometrial cancer (EC) is one of the most common malignant diseases worldwide. Although many studies have been performed on EC, a systems analysis between transcription factors (TFs) and EC relationship remains poorly characterized. Here, we present a systems bioinformatics analysis of TFs in EC patient samples to identify key TFs in EC. First, dysregulated and survival-related TFs were identified in EC using data from The Cancer Genome Atlas database and Gene Expression Omnibus. Second, we investigated the mechanisms of dysregulated TFs and tested whether their expression is correlated with prognosis of EC. Finally, we addressed new perspectives in EC biomarker research, including comprehensive knowledge of previously suggested candidate biomarkers in conjunction with novel mass spectrometry-based proteomic technologies with enhanced sensitivity and specificity not yet applied to EC studies, enabling a directed clinical perspective of the study design. Our study identified three promising TFs, E2F1, HMGA1, and PGR, which closely correlate with EC. Although treatments targeting TFs are not always efficient, these TFs may be useful as biomarkers for the diagnosis and prognosis of EC. Furthermore, we found that these dysregulated TFs and their target genes are primarily involved in the cell cycle and may promote endometrial carcinoma occurrence and development. Using integrated bioinformatic analysis, we identified candidate genes and pathways in EC, which could improve our understanding of the etiology and underlying molecular events of EC. Furthermore, these candidate genes and pathways could be therapeutic targets for EC.