A linear domain decomposition method for two-phase flow in porous media

This article is a follow up of our submitted paper [11] in which a decomposition of the Richards equation along two soil layers was discussed. A decomposed problem was formulated and a decoupling and linearisation technique was presented to solve the problem in each time step in a fixed point type iteration. This article extends these ideas to the case of two-phase in porous media and the convergence of the proposed domain decomposition method is rigorously shown.Comment: 8 page

Evidence for the Complexity of MicroRNA-Mediated Regulation in Ovarian Cancer: A Systems Approach

MicroRNAs (miRNAs) are short (∼22 nucleotides) regulatory RNAs that can modulate gene expression and are aberrantly expressed in many diseases including cancer. Previous studies have shown that miRNAs inhibit the translation and facilitate the degradation of their targeted messenger RNAs (mRNAs) making them attractive candidates for use in cancer therapy. However, the potential clinical utility of miRNAs in cancer therapy rests heavily upon our ability to understand and accurately predict the consequences of fluctuations in levels of miRNAs within the context of complex tumor cells. To evaluate the predictive power of current models, levels of miRNAs and their targeted mRNAs were measured in laser captured micro-dissected (LCM) ovarian cancer epithelial cells (CEPI) and compared with levels present in ovarian surface epithelial cells (OSE). We found that the predicted inverse correlation between changes in levels of miRNAs and levels of their mRNA targets held for only ∼11% of predicted target mRNAs. We demonstrate that this low inverse correlation between changes in levels of miRNAs and their target mRNAs in vivo is not merely an artifact of inaccurate miRNA target predictions but the likely consequence of indirect cellular processes that modulate the regulatory effects of miRNAs in vivo. Our findings underscore the complexities of miRNA-mediated regulation in vivo and the necessity of understanding the basis of these complexities in cancer cells before the therapeutic potential of miRNAs can be fully realized

Molecular Profiling Predicts the Existence of Two Functionally Distinct Classes of Ovarian Cancer Stroma

Although stromal cell signaling has been shown to play a significant role in the progression of many cancers, relatively little is known about its importance in modulating ovarian cancer development. The purpose of this study was to investigate the process of stroma activation in human ovarian cancer by molecular analysis of matched sets of cancer and surrounding stroma tissues. RNA microarray profiling of 45 tissue samples was carried out using the Affymetrix (U133 Plus 2.0) gene expression platform. Laser capture microdissection (LCM) was employed to isolate cancer cells from the tumors of ovarian cancer patients (Cepi) and matched sets of surrounding cancer stroma (CS). For controls, ovarian surface epithelial cells (OSE) were isolated from the normal (noncancerous) ovaries and normal stroma (NS). Hierarchical clustering of the microarray data resulted in clear separations between the OSE, Cepi, NS, and CS samples. Expression patterns of genes encoding signaling molecules and compatible receptors in the CS and Cepi samples indicate the existence of two subgroups of cancer stroma (CS) with different propensities to support tumor growth. Our results indicate that functionally significant variability exists among ovarian cancer patients in the ability of the microenvironment to modulate cancer development

Epigenetic changes within the promoter region of the HLA-G gene in ovarian tumors

© 2008 Menendez et al; licensee BioMed Central Ltd. The electronic version of this article is the complete one and can be found online at: http://www.molecular-cancer.com/content/7/1/43DOI: 10.1186/1476-4598-7-43Background: Previous findings have suggested that epigenetic-mediated HLA-G expression in tumor cells may be associated with resistance to host immunosurveillance. To explore the potential role of DNA methylation on HLA-G expression in ovarian cancer, we correlated differences in HLA-G expression with methylation changes within the HLA-G regulatory region in an ovarian cancer cell line treated with 5-aza-deoxycytidine (5-aza-dC) and in malignant and benign ovarian tumor samples and ovarian surface epithelial cells (OSE) isolated from patients with normal ovaries. Results: A region containing an intact hypoxia response element (HRE) remained completely methylated in the cell line after treatment with 5-aza-dC and was completely methylated in all of the ovarian tumor (malignant and benign) samples examined, but only variably methylated in normal OSE samples. HLA-G expression was significantly increased in the 5-aza-dC treated cell line but no significant difference was detected between the tumor and OSE samples examined. Conclusion: Since HRE is the binding site of a known repressor of HLA-G expression (HIF-1), we hypothesize that methylation of the region surrounding the HRE may help maintain the potential for expression of HLA-G in ovarian tumors. The fact that no correlation exists between methylation and HLA-G gene expression between ovarian tumor samples and OSE, suggests that changes in methylation may be necessary but not sufficient for HLA-G expression in ovarian cancer

Ovarian Cancer Detection from Metabolomic Liquid Chromatography/Mass Spectrometry Data by Support Vector Machines

© 2009 Guan et al; licensee BioMed Central Ltd. This article is available from: http://www.biomedcentral.com/1471-2105/10/259DOI:10.1186/1471-2105-10-259Background: The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease. Results: In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM. Conclusion: Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer

Epigenetic changes within the promoter region of the gene in ovarian tumors-3

reaction) on the ovarian carcinoma cell line BG-1, either untreated (control) or treated with 50 μM 5-aza-deoxycytidine (5-aza-dC). ("-" = samples without RT; "+" = samples with RT). was used as an endogenous control.<p><b>Copyright information:</b></p><p>Taken from "Epigenetic changes within the promoter region of the gene in ovarian tumors"</p><p>http://www.molecular-cancer.com/content/7/1/43</p><p>Molecular Cancer 2008;7():43-43.</p><p>Published online 22 May 2008</p><p>PMCID:PMC2429914.</p><p></p

Epigenetic changes within the promoter region of the gene in ovarian tumors-1

Ncers and regulator binding sites: HRE = hypoxia response element; B2 = enhancer κB2; B1 = enhancer κB1; ISRE = interferon sequence responsive element; W/S= W/S box; X1 = conserved X1 regulatory box; X2 = X2 box; CAAT = CCAAT box; TATA = TATA box; ex1 = exon 1; Met-F and Met-R = forward (F) and reverse (R) primer binding sites. Bisulfite genomic sequencing of 19 CpG dinucleotides of the region from -450 to ATG. Individual CpG dinulcotides are depicted as circles. Each row of circles represents an individual sequenced clone, either untreated (PBS) or treated with 50 μM 5-aza-dC (open circle = 100% unmethylated; filled circle = 100% methylated; %MET = percentage of methylation of each individual clone; %CONV = efficiency of sodium bisulfite treatment).<p><b>Copyright information:</b></p><p>Taken from "Epigenetic changes within the promoter region of the gene in ovarian tumors"</p><p>http://www.molecular-cancer.com/content/7/1/43</p><p>Molecular Cancer 2008;7():43-43.</p><p>Published online 22 May 2008</p><p>PMCID:PMC2429914.</p><p></p

Feasibility of Detecting Prostate Cancer by Ultra­performance Liquid Chromatography–Mass Spectrometry Serum Metabolomics

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, over­diagnosis, and over­treatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultra­performance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multi­variate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromato­graphically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lyso­phospho­lipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings