Gene Expression Analysis in Ovarian Cancer – Faults and Hints from DNA Microarray Study - Lisowska, Katarzyna M.; OIbryt, Magdalena; Dudaladava, Volha; Pamuła-Piłat, Jolanta; Kujawa, Katarzyna; Grzybowska, Ewa; Jarząb, Michał; Student, Sebastian; Rzepecka, Iwona K.; Jarząb, Barbara; Kupryjańczyk, Jolanta (Frontiers in Oncology, 2014)
The introduction of microarray techniques to cancer research brought great expectations for finding biomarkers that would improve patients treatment; however, the results of such studies are poorly reproducible, and critical analyses of these methods are rare. In this study we examined gene expression in 97 ovarian cancer samples. Validation of results by quantitative RT-PCR was performed also on 30 additional ovarian cancer samples. We carried out a number of systematic analyses in relation to several defined clinicopathological features. The main goal of our study was to delineate the molecular background of ovarian cancer chemoresistance and find biomarkers suitable for prediction of patients prognosis. Histological tumor type was the major source of variability in genes expression, except for serous and undifferentiated tumors that showed nearly identical profiles. Analysis of clinical endpoints (tumor response to chemotherapy, overall survival, disease-free survival, DFS) brought results that were not confirmed by validation either on the same group or on the independent group of patients. CLASP1 was the only gene that was found as important for DFS in the independent group, while in the preceding experiments it showed associations with other clinical endpoints and with BRCA1 gene mutation; thus, it may be worth of further testing. Our results confirm that histological tumor type is a strong confounding factor and gene expression studies of ovarian carcinomas should be performed on histologically homogeneous groups. Relatively small and unequal patients groups, may be a reason of poor reproducibility of statistical results, as well as the fact that the patients were treated with different chemotherapies. In addition, clinical endpoints may depend on subtle changes in many alternative molecular pathways, and this may be difficult to demonstrate by the methods applied. Also, arbitrarily performed division of samples may not reflect their true biological diversity