miRNAs in lung cancer - Studying complex fingerprints in patient's blood cells by microarray experiments

<p>Abstract</p> <p>Background</p> <p>Deregulated miRNAs are found in cancer cells and recently in blood cells of cancer patients. Due to their inherent stability miRNAs may offer themselves for blood based tumor diagnosis. Here we addressed the question whether there is a sufficient number of miRNAs deregulated in blood cells of cancer patients to be able to distinguish between cancer patients and controls.</p> <p>Methods</p> <p>We synthesized 866 human miRNAs and miRNA star sequences as annotated in the Sanger miRBase onto a microarray designed by febit biomed gmbh. Using the fully automated Geniom Real Time Analyzer platform, we analyzed the miRNA expression in 17 blood cell samples of patients with non-small cell lung carcinomas (NSCLC) and in 19 blood samples of healthy controls.</p> <p>Results</p> <p>Using t-test, we detected 27 miRNAs significantly deregulated in blood cells of lung cancer patients as compared to the controls. Some of these miRNAs were validated using qRT-PCR. To estimate the value of each deregulated miRNA, we grouped all miRNAs according to their diagnostic information that was measured by Mutual Information. Using a subset of 24 miRNAs, a radial basis function Support Vector Machine allowed for discriminating between blood cellsamples of tumor patients and controls with an accuracy of 95.4% [94.9%-95.9%], a specificity of 98.1% [97.3%-98.8%], and a sensitivity of 92.5% [91.8%-92.5%].</p> <p>Conclusion</p> <p>Our findings support the idea that neoplasia may lead to a deregulation of miRNA expression in blood cells of cancer patients compared to blood cells of healthy individuals. Furthermore, we provide evidence that miRNA patterns can be used to detect human cancers from blood cells.</p

MicroRNA expression in tumor cells from Waldenstrom's macroglobulinemia reflects both their normal and malignant cell counterparts

MicroRNAs (miRNAs) are involved in the regulation of many cellular processes including hematopoiesis, with the aberrant expression of differentiation-stage specific miRNA associated with lymphomagenesis. miRNA profiling has been essential for understanding the underlying biology of many hematological malignancies; however the miRNA signature of the diverse tumor clone associated with Waldenstrom's macroglobulinemia (WM), consisting of B lymphocytes, plasmacytes and lymphoplasmacytic cells, has not been characterized. We have investigated the expression of over 13 000 known and candidate miRNAs in both CD19+ and CD138+ WM tumor cells, as well as in their malignant and non-malignant counterparts. Although neither CD19+ nor CD138+ WM cells were defined by a distinct miRNA profile, the combination of all WM cells revealed a unique miRNA transcriptome characterized by the dysregulation of many miRNAs previously identified as crucial for normal B-cell lineage differentiation. Specifically, miRNA-9*/152/182 were underexpressed in WM, whereas the expression of miRNA-21/125b/181a/193b/223/363 were notably increased (analysis of variance; P<0.0001). Future studies focusing on the effects of these dysregulated miRNAs will provide further insight into the mechanisms responsible for the pathogenesis of WM

A hierarchical poisson log-normal model for network inference from RNA sequencing data.

Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data

MicroRNA dysregulation in the tumor microenvironment influences the phenotype of pancreatic cancer

Cellular interactions in the tumor microenvironment influence neoplastic progression in pancreatic ductal adenocarcinoma. One underlying mechanism is the induction of the prognostically unfavorable epithelial-mesenchymal-transition-like tumor budding. Our aim is to explore the expression of microRNAs implicated in the regulation of tumor budding focusing on the microenvironment of the invasive front. To this end, RNA from laser-capture-microdissected material of the main tumor, tumor buds, juxta-tumoral stroma, tumor-remote stroma, and non-neoplastic pancreatic parenchyma from pancreatic cancer cases with (n=7) and without (n=6) tumor budding was analyzed by qRT-PCR for the expression of a panel of miRNAs that are known to be implicated in the regulation of epithelial-mesenchymal transition, including miR-21, miR-183, miR-200b, miR-200c, miR-203, miR-205, miR-210, and miR-217. Here we show that at the invasive front of pancreatic ductal adenocarcinoma, specific microRNAs, are differentially expressed between tumor buds and main tumor cells and between cases with and without tumor budding, indicating their involvement in the regulation of the budding phenotype. Notably, miR-200b and miR-200c were significantly downregulated in the tumor buds. Consistent with this finding, they negatively correlated with the expression of epithelial-mesenchymal-transition-associated E-cadherin repressors ZEB1 and ZEB2 in the budding cells (P<0.001). Interestingly, many microRNAs were also dysregulated in juxta-tumoral compared to tumor-remote stroma suggesting that juxta-tumoral stroma contributes to microRNA dysregulation. Notably, miR-200b and miR-200c were strongly downregulated while miR-210 and miR-21 were upregulated in the juxta-tumoral vs tumor-remote stroma in carcinomas with tumor budding. In conclusion, microRNA targeting in both tumor and stromal cells could represent a treatment option for aggressive pancreatic cancer