Integrated bioinformatic analysis for endometriosis diagnosisMar 2, 2020
Early diagnosis of endometriosis: is it possible?
- Integrated bioinformatics analysis is a powerful and reliable tool to identify potential genes involved in the development of endometriosis.
- The genes related to cell migration and adhesion signaling pathways are important.
- Molecular mechanisms and key molecules involved in the development and perseverance of endometriosis will help appropriate armamentarium.
What’s done here?
- Using several mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes associated with the progress of endometriosis were identified. Additional bioinformatics tools were utilized to associate these DEGs with specific functions such as migration, adhesion, and hypoxia signaling.
- Integrated bioinformatics analysis identified 47 upregulated and 56 downregulated genes associated with endometriosis.
- Additional analyses revealed that these genes are especially related to migration, adhesion and hypoxia signaling.
- Random genes associated with each pathway were validated via immunohistochemistry showing consistency with the data obtained through bioinformatics analysis.
Limitations of the study:
- Data provided is obtained using data analysis only and additional research including clinical trials is required to validate the potential genes associated with endometriosis and achieve translation to the clinical relevance.
Endometriosis is considered as a non-malignant disease, but it is associated with several unfavorable conditions including non‑menstrual pelvic pain and infertility. Surgical resection and hormone suppression are common means of treatments; however, several side-effects and the recurrence are not preventable. There is an unmet need for developing therapeutic strategies by elucidating molecular mechanisms associated with endometriosis.
It is suggested that endometrium of women with endometriosis display an irregular molecular expression pattern, which might help the tissue with implantation, invasion, and development into a lesion. This study published by Dai et al. in the journal "Experimental and Therapeutic Medicine", applied integrated bioinformatics analysis to identify differentially expressed genes and determined potential molecular markers and signaling pathways associated with the development of endometriosis. Their approach resulted in the identification of 103 differentially expressed genes associated with endometriosis, of which 47 were upregulated and 56 were downregulated.
Cell adhesion, cell migration, cell to cell junction, heparin-binding, and hypoxia-inducible factor (HIF)-1 signaling are the main functional categories among the differentially expressed genes.
The study chose random genes associated with each pathway and validated them experimentally via immunohistochemistry, and confirmed that the approach is reliable.
More importantly, the study demonstrated that integrated bioinformatics is a powerful tool to identify the key molecules associated with endometriosis and might help to determine novel targets for its diagnosis and treatment.
Research Source: https://www.ncbi.nlm.nih.gov/pubmed/31853298
endometriosis bioinformatics differentially expressed genes early diagnosis