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 constitutively active heat shock transcription factor 1 (HSF1) in mouse spermatocytes induces apoptosis and leads to male infertility. We report here that prior to the onset of massive apoptosis caused by expression of active HSF1 in spermatocytes a marked reduction in spermatocyte-specific Hsp70.2 mRNA and protein levels occurs. In addition, HSP70.2 protein relocalizes from a predominant cytoplasmic to a nuclear position in developing spermatocytes that express active HSF1. Later in the developmental stages, cells undergoing HSF1-induced apoptosis essentially lack the HSP70.2 protein. The down-regulation of Hsp70.2 gene expression by HSF1 is paradoxical because HSF1 is the prototypical activator of HSP genes. Furthermore, HSF1-mediated repression neither involved a heat shock element (HSE)-like sequence adjacent to the Hsp70.2 gene nor were Hsp70.2 promoter sequences associated directly with HSF1. Interestingly, other spermatocyte- and spermatid-specific transcripts are also down-regulated in testes of transgenic mice expressing active HSF1, suggesting involvement of a putative HSF1-dependent block of development of spermatogenic cells. Importantly however, transcription of the Hsp70.2 gene is down-regulated in testes of wild-type mice subjected to a hyperthermia that induces transient activation of HSF1, indicating that the spermatocyte-specific activity of HSF1 might misdirect a network of transcription factors required for proper regulation of Hsp70.2.

There is an ongoing debate whether hereditary breast cancer is a clinical entity distinct from sporadic breast cancer. We tried to shed some light on this issue by comparing the molecular profiles of these two types of cancer using DNA microarrays. Our results show that a previously reported marked difference between BRCA1-mutation linked and sporadic breast cancer was probably due to uneven stratification of samples with different ER status and basal-like versus luminal-like subtype. We observed that apparent difference between BRCA1-linked and other types of breast cancer found in univariate analysis was diminished when data were corrected for ER status and molecular subtype in multivariate analyses. In fact, the difference in gene expression pattern of BRCA1-mutated and sporadic cancer is very discrete. These conclusions were supported by the results of Q-PCR validation. We also found that BRCA1 promoter hypermethylation had similar effect on global gene expression as mutation-induced protein truncation. Thus, in the molecular studies of hereditary breast cancer, BRCA1 promoter methylation should be recognized and considered together with gene mutation.

Global analysis of gene expression by DNA microarrays is nowadays a widely used tool, especially relevant for cancer research. It helps the understanding of complex biology of cancer tissue, allows identification of novel molecular markers, reveals previously unknown molecular subtypes of cancer that differ by clinical features like drug susceptibility or general prognosis. Our aim was to compare gene expression profiles in breast cancer that develop against a background of inherited predisposing mutations versus sporadic breast cancer. In this preliminary study we analysed seven hereditary, BRCA1 mutation-linked breast cancer tissues and seven sporadic cases that were carefully matched by histopathology and ER status. Additionally, we analysed 6 samples of normal breast tissue. We found that while the difference in gene expression profiles between tumour tissue and normal breast can be easily recognized by unsupervised algorithms, the difference between those two types of tumours is more discrete. However, by supervised methods of data analysis, we were able to select a set of genes that may differentiate between hereditary and sporadic tumours. The most significant difference concerns genes that code for proteins engaged in regulation of transcription, cellular metabolism, signalling, proliferation and cell death. Microarray results for chosen genes (TOB1, SEPHS2) were validated by real-time RT-PCR.