Serum microRNAs as non-invasive biomarkers for cancer

Human serum and other body fluids are rich resources for the identification of novel biomarkers, which can be measured in routine clinical diagnosis. microRNAs are small non-coding RNA molecules, which have an important function in regulating RNA stability and gene expression. The deregulation of microRNAs has been linked to cancer development and tumor progression. Recently, it has been reported that serum and other body fluids contain sufficiently stable microRNA signatures. Thus, the profiles of circulating microRNAs have been explored in a variety of studies aiming at the identification of novel non-invasive biomarkers

Differential expression of apoptotic genes PDIA3 and MAP3K5 distinguishes between low- and high-risk prostate cancer

<p>Abstract</p> <p>Background</p> <p>Despite recent progress in the identification of genetic and molecular alterations in prostate cancer, markers associated with tumor progression are scarce. Therefore precise diagnosis of patients and prognosis of the disease remain difficult. This study investigated novel molecular markers discriminating between low and highly aggressive types of prostate cancer.</p> <p>Results</p> <p>Using 52 microdissected cell populations of low- and high-risk prostate tumors, we identified via global cDNA microarrays analysis almost 1200 genes being differentially expressed among these groups. These genes were analyzed by statistical, pathway and gene enrichment methods. Twenty selected candidate genes were verified by quantitative real time PCR and immunohistochemistry. In concordance with the mRNA levels, two genes <it>MAP3K5 </it>and <it>PDIA3 </it>exposed differential protein expression. Functional characterization of <it>PDIA3 </it>revealed a pro-apoptotic role of this gene in PC3 prostate cancer cells.</p> <p>Conclusions</p> <p>Our analyses provide deeper insights into the molecular changes occurring during prostate cancer progression. The genes <it>MAP3K5 </it>and <it>PDIA3 </it>are associated with malignant stages of prostate cancer and therefore provide novel potential biomarkers.</p

ERG Induces Epigenetic Activation of Tudor Domain-Containing Protein 1 (TDRD1) in ERG Rearrangement-Positive Prostate Cancer

Background Overexpression of ERG transcription factor due to genomic ERG- rearrangements defines a separate molecular subtype of prostate tumors. One of the consequences of ERG accumulation is modulation of the cell’s gene expression profile. Tudor domain-containing protein 1 gene (TDRD1) was reported to be differentially expressed between TMPRSS2:ERG-negative and TMPRSS2:ERG-positive prostate cancer. The aim of our study was to provide a mechanistic explanation for the transcriptional activation of TDRD1 in ERG rearrangement-positive prostate tumors. Methodology/Principal Findings Gene expression measurements by real-time quantitative PCR revealed a remarkable co-expression of TDRD1 and ERG (r2 = 0.77) but not ETV1 (r2<0.01) in human prostate cancer in vivo. DNA methylation analysis by MeDIP-Seq and bisulfite sequencing showed that TDRD1 expression is inversely correlated with DNA methylation at the TDRD1 promoter in vitro and in vivo (ρ = −0.57). Accordingly, demethylation of the TDRD1 promoter in TMPRSS2:ERG-negative prostate cancer cells by DNA methyltransferase inhibitors resulted in TDRD1 induction. By manipulation of ERG dosage through gene silencing and forced expression we show that ERG governs loss of DNA methylation at the TDRD1 promoter-associated CpG island, leading to TDRD1 overexpression. Conclusions/Significance We demonstrate that ERG is capable of disrupting a tissue-specific DNA methylation pattern at the TDRD1 promoter. As a result, TDRD1 becomes transcriptionally activated in TMPRSS2:ERG-positive prostate cancer. Given the prevalence of ERG fusions, TDRD1 overexpression is a common alteration in human prostate cancer which may be exploited for diagnostic or therapeutic procedures

Application of a functional model for the modernization of devices designed to eliminate emergency oil spills

The article describes the stages of creation and composition of the functional model, which can be used for the design of oil spill response devices. The principle of operation of the functional model given in the article and it's graphical scheme are show

Loss of aquaporin-4 expression and putative function in non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Aquaporins (AQPs) have been recognized to promote tumor progression, invasion, and metastasis and are therefore recognized as promising targets for novel anti-cancer therapies. Potentially relevant AQPs in distinct cancer entities can be determined by a comprehensive expression analysis of the 13 human AQPs.</p> <p>Methods</p> <p>We analyzed the presence of all AQP transcripts in 576 different normal lung and non-small cell lung cancer (NSCLC) samples using microarray data and validated our findings by qRT-PCR and immunohistochemistry.</p> <p>Results</p> <p>Variable expression of several AQPs (AQP1, -3, -4, and -5) was found in NSCLC and normal lung tissues. Furthermore, we identified remarkable differences between NSCLC subtypes in regard to AQP1, -3 and -4 expression. Higher transcript and protein levels of AQP4 in well-differentiated lung adenocarcinomas suggested an association with a more favourable prognosis. Beyond water transport, data mining of co-expressed genes indicated an involvement of AQP4 in cell-cell signalling, cellular movement and lipid metabolism, and underlined the association of AQP4 to important physiological functions in benign lung tissue.</p> <p>Conclusions</p> <p>Our findings accentuate the need to identify functional differences and redundancies of active AQPs in normal and tumor cells in order to assess their value as promising drug targets.</p

Classification across gene expression microarray studies

<p>Abstract</p> <p>Background</p> <p>The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.</p> <p>Results</p> <p>For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios.</p> <p>Conclusions</p> <p>We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.</p

TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-β signaling

<p>Abstract</p> <p>Background</p> <p><it>TMPRSS2-ERG </it>gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although <it>TMPRSS2-ERG </it>fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear.</p> <p>Methods</p> <p>We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays.</p> <p>Results</p> <p>Comparison of gene expression levels among <it>TMPRSS2-ERG </it>fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like <it>CRISP3 </it>were found to be associated with the gene fusion status. WNT and TGF-β/BMP signaling pathways were significantly associated with genes upregulated in <it>TMPRSS2-ERG </it>fusion-positive tumors.</p> <p>Conclusions</p> <p>The <it>TMPRSS2-ERG </it>gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.</p

Identifizierung differenziell exprimierter Gene bei Brust- und Ovarialkarzinomen in den chromosomalen Regionen 1q32-q41 und 11q12-q23

Brust- und Ovarialkarzinome gehören zu den häufigsten sporadischen Tumorerkrankungen bei der Frau. Die Progression von benignen Neoplasien zu malignen Karzinomen werden von spezifischen Veränderungen der Genexpression begleitet. Durch die bioinformatische Auswertung von vier Millionen ESTs aus cDNA-Bibliotheken von Normalgeweben und der korrespondierenden Tumore wurden Hunderte von differenziell exprimierten Genen selektiert. Nach der chromosomalen Kartierung der Kandidatengene erfolgten weitere Untersuchungen für Gene aus den chromosomalen Regionen 1q32-q41 und 11q12-q23, die häufig in Brust- und Ovarialkarzinomen als aberrant beschrieben wurden. Die Validierung der in-silico Expressionsdaten erfolgte über Northernblot- und cDNA-Array-Analysen von unselektierten und mikrodissezierten Tumorproben. Es konnte für einige der Gene die differenzielle Expression in Brusttumoren bestätigt und dadurch neue Kandidatengene für die untersuchten Genloci identifiziert werden. Das Expressionsmuster einer Acyltransferase in 11q13 als potenzielles Tumorsuppressorgen erbrachte den Hinweis auf eine mögliche Involvierung in den Retinol-Metabolismus und könnte auf einen Mechanismus der Inhibierung der Karzinogenese im Brustepithel hindeuten.Breast and ovarian cancers have become major tumor diseases among woman. The progression of benign neoplasia to malignant carcinoma is characterized by specific changes of gene expression. By using an in-silico strategy to analyze four million ESTs available in cDNA libraries of normal and the corresponding tumor tissues, we selected hundreds of differentially expressed genes. After chromosomal assignment of the candidate genes, further experiments were focussed on these genes located in the specific regions 1q32-q41 and 11q13-q23 often described to be aberrant in breast and ovarian cancer specimen. The in-silico expression analysis was verifyed by Northern blot and cDNA-Array techniques of unselected and microdissected tumor samples. We could confirm the in-silico expression pattern of some genes and identified novel tumor associated candidate genes for the investigated loci. According to the expression pattern, an acyltransferases located in 11q13 may represent a tumor suppressor gene, which could possibly inhibit breast carcinogenesis by involving in retinol metabolism

Lung Cancer Gene Signatures and Clinical Perspectives

Microarrays have been used for more than two decades in preclinical research. The tumor transcriptional profiles were analyzed to select cancer-associated genes for in-deep functional characterization, to stratify tumor subgroups according to the histopathology or diverse clinical courses, and to assess biological and cellular functions behind these gene sets. In lung cancer—the main type of cancer causing mortality worldwide—biomarker research focuses on different objectives: the early diagnosis of curable tumor diseases, the stratification of patients with prognostic unfavorable operable tumors to assess the need for further therapy regimens, or the selection of patients for the most efficient therapies at early and late stages. In non-small cell lung cancer, gene and miRNA signatures are valuable to differentiate between the two main subtypes’ squamous and non-squamous tumors, a discrimination which has further implications for therapeutic schemes. Further subclassification within adenocarcinoma and squamous cell carcinoma has been done to correlate histopathological phenotype with disease outcome. Those tumor subgroups were assigned by diverse transcriptional patterns including potential biomarkers and therapy targets for future diagnostic and clinical applications. In lung cancer, none of these signatures have entered clinical routine for testing so far. In this review, the status quo of lung cancer gene signatures in preclinical and clinical research will be presented in the context of future clinical perspectives