Related Experiment Video
Updated: May 16, 2026
Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
Weighted gene co-expression network analysis in identification of endometrial cancer prognosis markers
Xiao-Lu Zhu1, Zhi-Hong Ai, Juan Wang
1Department of Obstetrics and Gynecology, Shanghai Sixth People's Hospital, Shanghai Jiaotong University, Shanghai, China.
Objective:
Endometrial cancer (EC) is the most common gynecologic malignancy. Identification of potential biomarkers of EC would be helpful for the detection and monitoring of malignancy, improving clinical outcomes.
Methods:
The Weighted Gene Co-expression Network Analysis method was used to identify prognostic markers for EC in this study. Moreover, underlying molecular mechanisms were characterized by KEGG pathway enrichment and transcriptional regulation analyses.
Results:
Seven gene co-expression modules were obtained, but only the turquoise module was positively related with EC stage. Among the genes in the turquoise module, COL5A2 (collagen, type V, alpha 2) could be regulated by PBX (pre-B-cell leukemia homeobox 1)1/2 and HOXB1(homeobox B1) transcription factors to be involved in the focal adhesion pathway; CENP-E (centromere protein E, 312kDa) by E2F4 (E2F transcription factor 4, p107/p130-binding); MYCN (v-myc myelocytomatosis viral related oncogene, neuroblastoma derived [avian]) by PAX5 (paired box 5); and BCL-2 (B-cell CLL/ lymphoma 2) and IGFBP-6 (insulin-like growth factor binding protein 6) by GLI1. They were predicted to be associated with EC progression via Hedgehog signaling and other cancer related-pathways.
Conclusions:
These data on transcriptional regulation may provide a better understanding of molecular mechanisms and clues to potential therapeutic targets in the treatment of EC.