Related Experiment Video
Updated: May 20, 2025
Analyzing Oxidative Stress in Murine Intestinal Organoids using Reactive Oxygen Species-Sensitive Fluorogenic Probe
Published on: September 17, 2021
Identification and analysis of oxidative stress-related genes in endometriosis
Chengmao Xie1, Chang Lu1, Na Lv1
1Department of Gynecology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China.
Background:
Early diagnosis and treatment of endometriosis (EM) remain challenging because of the lack of knowledge about EM development. While oxidative stress (OS) has been associated with EM, the link is unclear. We explored OS-related genes (OSRGs) and their role in EM pathogenesis.
Material And Methods:
We combined two ectopic endometrium (EC) and eutopic endometrium (EU) datasets (GSE11691 and GSE25628) into a dataset for analysis. Bioinformatic analyses were used to identify differentially expressed genes (DEGs), OS-related genes (OSRGs), enriched pathways, competitive endogenous RNA network, and immune cell infiltration. Finally, real time-quantitative polymerase chain reaction (RT-qPCR) and Western blot (WB) were used to validate the expression of key OSRGs in clinical patient samples.
Results:
Bioinformatic analysis identified 459 DEGs between EC and EU samples, including 67 OSRGs. A ceRNA network was established, encompassing 28 DE-OSRGs, 32 miRNAs, and 53 lncRNAs. Four key OSRGs (CYP17A1, NR3C1, ENO2, and NGF) were selected from protein-protein interaction network analysis. The RT-qPCR and WB analysis showed that these genes' abnormal changes in RNA and protein levels were consistent with data in public databases. Weighted gene co-expression network analysis identified three immune-related OSRGs (CYP17A1, NR3C1, and NGF) and 20 lncRNAs that may regulate NR3C1 through 10 miRNAs.
Conclusion:
The key OSRGs may function via multilayered networks in EM. We provide insights into EM and underscore the potential significance of OSRGs and the immune environment for diagnostic and prognosis evaluation.
