The Deregulated Molecular Functions Involved in Malignant Transformation of Endometriosis to Endometriosis-Associated Ovarian CarcinomaBy: Dr. Youngran Park - Jan 16, 2018
Malignant transformation of endometriosis-associated ovarian carcinoma
The deregulated oxidoreductase activity, metabolism, hormone activity, inflammatory response, innate immune response and cell-cell signaling play the key roles in the malignant transformation of endometriosis-associated ovarian carcinoma (EAOC) from endometriosis.
- This group established a bioinformatic platform of function-based, data-driven analysis of the molecular "functionomes" to dissect the molecular pathogenetic pathways of EAOC.
- Data supported the postulation that endometriosis shares similar molecular signatures with EAOC, which was validated by data-driven analysis.
What's done here:
- They used the microarray gene expression datasets of endometriosis, CCC and EC downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database.
- The pathogenesis of EAOC investigated by a data-driven, function-based analytic model with the quantified molecular functions defined by 1454 Gene Ontology (GO) term gene sets.
- This result showed that the inflammation and immune-related GO terms, including “MAP kinase kinase kinase activity” and “activation of immune response”, significantly deregulated among endometriosis, CCC, and EC.
- Exploratory factor analysis revealed the crucial elements involving in the pathogenesis network of ES, such as “response to hormone”, “endothelial cell proliferation”, “inflammation response”, “immune response”, “regulation of MAPK cascade” and “oxidative stress”.
To support the significance of identified "functionomes" clinically, further immunohistochemical staining and functional validations using clinical samples are required.
Endometriosis-associated ovarian cancer (EAOC) is postulated to be transformed from endometriosis. Though different deregulated functional areas were proposed accounting for the pathogenesis of EAOC transformation, there is a lack of data-driven analysis to prove this. To understand the EAOC pathogenesis better, this group conducted the function-based, data-driven study to investigate complex diseases with the functionomes. They used microarray gene expression datasets of endometriosis, CCC, and EC from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database. The group demonstrated that the inflammatory/immune response, oxidative stress and hormone activity play an interactive role in modulating the malignant transformation and cancer progression in EAOC. It is also suggested that the deregulated molecular functionomes may provide as molecular biomarkers in monitoring the malignant transformation of endometriosis in the future.
Research Source: https://www.ncbi.nlm.nih.gov/pubmed/29113136
endometriosis ovarian carcinoma function-based data-driven analysis microarray gene expression datasets Gene Ontology