RESEARCH PAPER
Microtissue density prognostic factor evaluation based on antigens CD34 and CD 105 in ovarian cancer patients
| 1 | Railway Hospital, Lublin, Poland |
| 2 | Department of Gynaecology and Endocrynological Gynaecology, Department of Gynaecological Oncology and Gynaecology, Medical University, Lublin, Poland |
| 3 | Department of Gynaecological Oncology and Gynaecology, Medical University, Lublin, Poland |
| 4 | Department of Gynecological Oncology and Gynecology, Medical University, Lublin, Poland |
Ann Agric Environ Med. 2013;20(4):838–842
KEYWORDS
ABSTRACT
Uncontrollable cell division and disorders of the apoptotic processes constitute the key phenomena in cancer transformation. The theory that the tumour growth above critical density is possible due to creation of the new blood vessels during angiogenesis process was put forward in 1971 by Folkman. The panendotelial antibodies targeted against such markers as CD34 are used most frequently in cancer vessel evaluation. The anti-CD34 reacts with the largest number of endoepithelial cells. The second group constitutes the antibodies that agglomerate with the antigens characteristic for proliferous endoepithelial cells. The most popular marker used for functional endothelial tissues is endoglin called CD105. The subject of this publication is to find the answer to a question whether the practical usage of the CD34 and CD 105 as a prognostic factor in predicting failure of a planned treatment, determining expected remission and the total survival rate is possible. 74 patients with the diagnosed ovarian cancer, treated in the I Clinic of Gynecology Oncology and Gynecology, Medical University in Lublin, between years 1999–2004 were included into the analysis. Representative paraffin blocks with the embedded ovarian cancer fragments were used for immunohistochemical research. Density of the microvessels was being evaluated basing on the expression of the antigen CD34 and CD105. Evaluation of the microvessel density with CD34 and CD105 markers is not useful in forecasting survival rate and disease recurrence in patients with ovary cancer.
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