Proteomic Analysis of Low-Grade Serous Ovarian Cancer and Comparing It with Non-cancerous Ovarian
Background In Iran, ovarian cancer is the eighth most common cancer. Serous is considered as the most common of all ovarian cancers. It is usually deadly due to late diagnosis. Moreover, as all studies so far have considered metastatic or high-stage ovarian cancer, this study sought low-grade ovaria...
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Veröffentlicht in: | Indian journal of gynecologic oncology 2018-06, Vol.16 (2), Article 22 |
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Sprache: | eng |
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Zusammenfassung: | Background
In Iran, ovarian cancer is the eighth most common cancer. Serous is considered as the most common of all ovarian cancers. It is usually deadly due to late diagnosis. Moreover, as all studies so far have considered metastatic or high-stage ovarian cancer, this study sought low-grade ovarian serous in human, which is of high importance and aimed at obtaining protein biomarkers that may be useful in diagnosis and treatment of ovarian serous cancer.
Materials and Methods
In this experimental study, ten patients with proved low-grade serous ovarian cancer and ten women without ovarian cancer, healthy, and cancerous tissue samples were obtained to perform proteomics. All tissue proteins were purified using standard separation methods, and proteins were separated using two-dimensional electrophoresis. Healthy and tumorous tissue proteomes were compared, and the extent of protein expression was analyzed.
Results
This study identified 212 proteins, 41 of which had changes in protein expression in cancer state. The expression of eight proteins was suppressed, and five proteins were expressed in cancerous group. Thirteen proteins in cancer sample showed increase in expression and 15 proteins reduced expression in cancerous tissue.
Conclusion
It seems that in this disease, the expression of a large number of proteins in cancerous tissues changed, most of them decreased or not expressed at all. These changed proteins can be effective as biomarker in diagnosis and treatment of this disease. |
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ISSN: | 2363-8397 2363-8400 |
DOI: | 10.1007/s40944-018-0187-8 |