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

A genetic map for the model legume Lotus japonicus has been developed. The F₂ mapping population was established from an interspecific cross between L. japonicus and L. filicaulis. A high level of DNA polymorphism between these parents was the source of markers for linkage analysis and the map is based on a framework of amplified fragment length polymorphism (AFLP) markers. Additional markers were generated by restriction fragment length polymorphism (RFLP) and sequence-specific PCR. A total of 524 AFLP markers, 3 RAPD markers, 39 gene-specific markers, 33 microsatellite markers, and six recessive symbiotic mutant loci were mapped. This genetic map consists of six linkage groups corresponding to the six chromosomes in L. japonicus. Fluorescent in situ hybridization (FISH) with selected markers aligned the linkage groups to chromosomes as described in the accompanying article by Pedrosa et al. ( 2002, this issue). The length of the linkage map is 367 cM and the average marker distance is 0.6 cM. Distorted segregation of markers was found in certain sections of the map and linkage group I could be assembled only by combining colormapping and cytogenetics (FISH). A fast method to position genetic loci employing three AFLP primer combinations yielding 89 markers was developed and evaluated by mapping three symbiotic loci, Ljsym1, Ljsym5, and Ljhar1-3.

The putative oncogene, CRNDE , is a negative prognostic factor in ovarian cancer patients

Purpose Ovarian cancer is typically diagnosed at late stages, and thus, patients’ prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets. Methods An unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR. Results We found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clustering based on the expression of these genes revealed two clusters of ovarian cancers with different molecular profiles and distinct overall survival (OS). Patients with higher expression of these genes had shorter OS than those with lower expression. The two clusters did not derive from high- versus low-grade serous carcinomas and were unrelated to histological (ovarian vs. fallopian) origin. Interestingly, there was considerable overlap between identified prognostic signature and a recently described invasion-associated signature related to stromal desmoplastic reaction. Several genes from this signature were validated by quantitative PCR; two of them—DSPG3 and LOX—were validated both in the initial and independent sets of samples and were significantly associated with OS and disease-free survival. Conclusions We distinguished two molecular subgroups of serous ovarian cancers characterized by distinct OS. Among differentially expressed genes, some may potentially be used as prognostic markers. In our opinion, unsupervised methods of microarray data analysis are more effective than supervised methods in identifying intrinsic, biologically sound sources of variability. Moreover, as histological type of the tumor is the greatest source of variability in ovarian cancer and may interfere with analyses of other features, it seems reasonable to use histologically homogeneous groups of tumors in microarray experiments.

Hypoxia is one of the most important features of the tumor microenvironment, exerting an adverse effect on tumor aggressiveness and patient prognosis. Two types of hypoxia may occur within the tumor mass, chronic (prolonged) and cycling (transient, intermittent) hypoxia. Cycling hypoxia has been shown to induce aggressive tumor cell phenotype and radioresistance more significantly than chronic hypoxia, though little is known about the molecular mechanisms underlying this phenomenon. The aim of this study was to delineate the molecular response to both types of hypoxia induced experimentally in tumor cells, with a focus on cycling hypoxia. We analyzed in vitro gene expression profile in three human cancer cell lines (melanoma, ovarian cancer, and prostate cancer) exposed to experimental chronic or transient hypoxia conditions. As expected, the cell-type specific variability in response to hypoxia was significant. However, the expression of 240 probe sets was altered in all 3 cell lines. We found that gene expression profiles induced by both types of hypoxia were qualitatively similar and strongly depend on the cell type. Cycling hypoxia altered the expression of fewer genes than chronic hypoxia (6,132 vs. 8,635 probe sets, FDR adjusted p<0.05), and with lower fold changes. However, the expression of some of these genes was significantly more affected by cycling hypoxia than by prolonged hypoxia, such as IL8, PLAU, and epidermal growth factor (EGF) pathway-related genes (AREG, HBEGF, and EPHA2). These transcripts were, in most cases, validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Our results indicate that experimental cycling hypoxia exerts similar, although less intense effects, on the examined cancer cell lines than its chronic counterpart. Nonetheless, we identified genes and molecular pathways that seem to be preferentially regulated by cyclic hypoxia.