BackgroundMost patients with ovarian cancer are diagnosed with advanced stage disease (i.e., stage III-IV), which is associated with a poor prognosis. Differentially expressed genes (DEGs) in stage III serous ovarian carcinoma compared to normal tissue were screened by a new differential display method, the annealing control primer (ACP) system. The potential targets for markers that could be used for diagnosis and prognosis, for stage III serous ovarian cancer, were found by cluster and survival analysis.MethodsThe ACP-based reverse transcriptase polymerase chain reaction (RT PCR) technique was used to identify DEGs in patients with stage III serous ovarian carcinoma. The DEGs identified by the ACP system were confirmed by quantitative real-time PCR. Cluster analysis was performed on the basis of the expression profile produced by quantitative real-time PCR and survival analysis was carried out by the Kaplan-Meier method and Cox proportional hazards multivariate model; the results of gene expression were compared between chemo-resistant and chemo-sensitive groups.ResultsA total of 114 DEGs were identified by the ACP-based RT PCR technique among patients with stage III serous ovarian carcinoma. The DEGs associated with an apoptosis inhibitory process tended to be up-regulated clones while the DEGs associated with immune response tended to be down-regulated clones. Cluster analysis of the gene expression profile obtained by quantitative real-time PCR revealed two contrasting groups of DEGs. That is, a group of genes including: SSBP1, IFI6 DDT, IFI27, C11orf92, NFKBIA, TNXB, NEAT1 and TFG were up-regulated while another group of genes consisting of: LAMB2, XRCC6, MEF2C, RBM5, FOXP1, NUDCP2, LGALS3, TMEM185A, and C1S were down-regulated in most patients. Survival analysis revealed that the up-regulated genes such as DDAH2, RNase K and TCEAL2 might be associated with a poor prognosis. Furthermore, the prognosis of patients with chemo-resistance was predicted to be very poor when genes such as RNase K, FOXP1, LAMB2 and MRVI1 were up-regulated.ConclusionThe DEGs in patients with stage III serous ovarian cancer were successfully and reliably identified by the ACP-based RT PCR technique. The DEGs identified in this study might help predict the prognosis of patients with stage III serous ovarian cancer as well as suggest targets for the development of new treatment regimens.

Background: The thermotolerance of Aspergillus fumigatus plays a critical role in mammalian and avian infections. Thus, the identification of its adaptation mechanism to higher temperature is very important for an efficient anti-fungal drug development as well as fundamental understanding of its pathogenesis. We explored the temporal transcription regulation structure of this pathogenic fungus under heat shock conditions using the time series microarray data reported by Nierman et al. (Nature 2005, 438:1151-1156.

Glutathione peroxidase 3 (GPX3) is a member of glutathione peroxidase family, exerting one of the most important cellular defense mechanisms against stress signals, including oxidative damage. In this study, the expression of GPX3 mRNA and protein was analyzed for ovarian cancer tissues to test its applicability as a biomarker that can distinguish the four major histologic types of epithelial ovarian cancer. A public microarray dataset containing 99 ovarian cancer and 4 normal ovary samples was downloaded, and GPX3 mRNA expression was analyzed. The expression of GPX3 protein was measured by immunohistochemical staining in 40 epithelial ovarian cancer tissues, 10 for each of the serous, endometrioid, mucinous, and clear cell type. Histoscores were made from the immunohistostaining, and analysis of variance (ANOVA) was performed to quantitate the differences in protein level. Analysis of genomic dataset confirms a GPX3 overexpression in clear cell type ovarian adenocarcinoma compared with normal ovary and 3 other subtypes of epithelial ovarian cancer at mRNA level. GPX3 also shows the highest average antibody staining intensities in clear cell type ovarian adenocarcinomas over the other 3 types in immunostaining on tissue arrays. This is the first validation of GPX3 as a clear cell type-specific biomarker in ovarian cancer patients' tissues by immunostaining. GPX3 may serve as an important molecular marker for the diagnosis and molecular understanding of clear cell carcinoma of the ovary.

Since persistent infection with high-risk human papillomavirus (HPV) is a known cause of highgrade cervical intraepithelial neoplasia and cervical cancer, several HPV DNA detection methods have been developed during the last decade. The Hybrid Capture II (HCII) assay, which allows detection of 13 high-risk HPVs, has been well validated; however, it does not provide any genotype-specific information. The oncogenic activity of HPV is dependent on its genotype. The prophylactic effects of HPV vaccines are based on L1 virus-like particles and are limited mainly to infections corresponding to the HPV type used to develop the immunogen. Therefore, accurate detection and genotyping are important for treatment as well as screening. A newly developed HPV genotyping system using a liquid bead array was evaluated with 286 cervical samples and the results were compared to two commercially available methods, i.e. the HCII and HPV DNA chip assays, and sequencing. The sensitivity for detection of high-risk HPV was 85.3 % (HCII), 94.7 % (DNA chip) and 99.0 % (liquid bead array). The liquid bead array showed almost perfect agreement (k50.95) with genotype information confirmed by sequencing, while substantial agreement (k50.8) was observed between DNA chip and sequencing. Furthermore, the liquid bead array had superior detection of 26 HPVs (16 high-risk and 10 low-risk types) and has proven to be as accurate as sequencing in identifying individual HPV types, even in cases with multiple HPV infections.

An immense variety of complex secondary metabolites is produced by filamentous fungi including Aspergillus fumigatus, a main inducer of invasive aspergillosis. The identification of fungal secondary metabolite gene cluster is essential for the characterization of fungal secondary metabolism in terms of genetics and biochemistry through recombinant technologies such as gene disruption and cloning. Most of the prediction methods for secondary metabolite gene cluster severely depend on homology searches. However, homology-based approach has intrinsic limitation to unknown or novel gene cluster. We analyzed the GC and window-averaged DNA curvature profile of 26 secondary metabolite gene clusters in the A. fumigatus genome to find out potential conserved features of secondary metabolite gene cluster. Fifteen secondary metabolite gene clusters showed a conserved pattern in window-averaged DNA curvature profile, that is, the DNA regions including secondary metabolic signature genes such as polyketide synthase, nonribosomal peptide synthase, and/or dimethylallyl tryptophan synthase consisted of window-averaged DNA curvature values lower than 0.18 and these DNA regions were at least 20 kb. Forty percent of secondary metabolite gene clusters with this conserved pattern were related to severe regulation by a transcription factor, LaeA. Our result could be used for identification of other fungal secondary metabolite gene clusters, especially for secondary metabolite gene cluster that is severely regulated by LaeA or other proteins with similar function to LaeA.