Gene Expression Analysis in Ovarian Cancer – Faults and Hints from DNA Microarray Study

Abstract

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 global gene expression in 97 ovarian cancer samples. Also, validation of results by quantitative RT-PCR was performed 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. We found that 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 to be important for DFS in the independent group, whereas in the preceding experiments it showed associations with other clinical endpoints and with BRCA1 gene mutation; thus, it may be worthy of further testing. Our results confirm that histological tumor type may be a strong confounding factor and we conclude that gene expression studies of ovarian carcinomas should be performed on histologically homogeneous groups. Among the reasons of poor reproducibility of statistical results may be the fact that despite relatively large patients’ group, in some analyses one has to compare small and unequal classes of samples. In addition, arbitrarily performed division of samples into classes compared may not always reflect their true biological diversity. And finally, we think that clinical endpoints of the tumor probably depend on subtle changes in many and, possibly, alternative molecular pathways, and such changes may be difficult to demonstrate.

Expression of thrombospondin-1 by tumor cells in patient-derived ovarian carcinoma xenografts

“…Most of the overrepresented biological processes (p50.05) were related to regulation of angiogenesis and blood vessel formation, peptidase activity, signaling, cell functions, such as motility, cell cycle and death, cell shape, coagulation and communication, suggesting an involvement of TSP-1 in these aspects of ovarian tumor progression (Figure 4). The expression of TSP-1 was also analyzed in a large external gene expression dataset, retrieved from GEO (GSE63885, NCBI database), comprising 101 ovarian cancer surgical samples (31). In these clinical samples, TSP1 resulted slightly differentially expressed according to histotype (Kruskal-Wallis test, p ¼ 0.059), with higher expression in serous versus endometrioid cancers ( Figure 5), as already observed in our PDX cohort.…”

Targeting the SR-B1 Receptor as a Gateway for Cancer Therapy and Imaging

“…However, a reverse was observed in cancers such ovarian, osteosarcoma, B-Cell lymphoma, and lung (Bild et al, 2006; Kuijjer et al, 2012; Lisowska et al, 2014), specifically low SR-B1 correlated with poor survival. Given the complexities involved in the regulation of SR-B1 expression it is difficult to hypothesize regarding mechanism(s) involved in the observed variations of SR-B1 expression in relation to patient survival.…”

Panel of seven long noncoding RNA as a candidate prognostic biomarker for ovarian cancer

“…Using the publicly released ovarian cancer dataset (GSE9891)15 from the Gene Expression Omnibus (GEO) database, we identified a signature including seven lncRNAs associating with survival, and proposed a risk score formula based on their expressions to predict the patient survival. The prognostic risk score model was further validated in the cohorts GSE26193 and GSE63885 16,17. Our findings indicated that the seven-lncRNA signature can serve as a strong prognostic biomarker for ovarian cancer.…”