Differential diagnosis of hepatocellular carcinoma from metastatic tumors in the liver using microRNA expression

Distinguishing hepatocellular carcinoma from metastatic tumors in the liver is of great practical importance, with significant therapeutic and prognostic implications. This differential diagnosis can be difficult because metastatic cancers in the liver, especially adenocarcinomas, may mimic the morphology and immunoexpression of hepatocellular carcinoma. Biomarkers that are specifically expressed in either hepatocellular carcinoma or metastatic adenocarcinoma can therefore be useful diagnostic tools. To find such biomarkers, we studied microRNA expression in 144 tumor samples using custom microarrays. Hsa-miR-141 and hsa-miR-200c, microRNAs that promote epithelial phenotypes, had significantly higher levels in non-hepatic epithelial tumors. In contrast, endothelial-associated hsa-miR-126 showed higher expression levels in hepatocellular carcinomas. Combinations of these microRNAs accurately identified primary hepatocellular carcinoma from metastatic adenocarcinoma in the liver. These findings were validated using quantitative real-time PCR to measure microRNA expression in additional samples. Thus, the tissue-specific expression patterns of microRNAs make them useful biomarkers for the diagnosis of liver malignancies.     

Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network

Background

Recent studies have shown that the regulatory effect of microRNAs can be investigated by examining expression changes of their target genes. Given this, it is useful to define an overall metric of regulatory effect for a specific microRNA and see how this changes across different conditions.

Results

Here, we define a regulatory effect score (RE-score) to measure the inhibitory effect of a microRNA in a sample, essentially the average difference in expression of its targets versus non-targets. Then we compare the RE-scores of various microRNAs between two breast cancer subtypes: estrogen receptor positive (ER⁺) and negative (ER^-). We applied this approach to five microarray breast cancer datasets and found that the expression of target genes of most microRNAs was more repressed in ER^- than ER⁺; that is, microRNAs appear to have higher RE-scores in ER^- breast cancer. These results are robust to the microRNA target prediction method. To interpret these findings, we analyzed the level of microRNA expression in previous studies and found that higher microRNA expression was not always accompanied by higher inhibitory effects. However, several key microRNA processing genes, especially Ago2 and Dicer, were differentially expressed between ER^- and ER⁺ breast cancer, which may explain the different regulatory effects of microRNAs in these two breast cancer subtypes.

Conclusions

The RE-score is a promising indicator to measure microRNAs' inhibitory effects. Most microRNAs exhibit higher RE-scores in ER^- than in ER⁺ samples, suggesting that they have stronger inhibitory effects in ER^- breast cancers.     

mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer

Background

Recent studies have shown that the regulatory effect of microRNAs can be investigated by examining expression changes of their target genes. Given this, it is useful to define an overall metric of regulatory effect for a specific microRNA and see how this changes across different conditions.

Results

Here, we define a regulatory effect score (RE-score) to measure the inhibitory effect of a microRNA in a sample, essentially the average difference in expression of its targets versus non-targets. Then we compare the RE-scores of various microRNAs between two breast cancer subtypes: estrogen receptor positive (ER⁺) and negative (ER^-). We applied this approach to five microarray breast cancer datasets and found that the expression of target genes of most microRNAs was more repressed in ER^- than ER⁺; that is, microRNAs appear to have higher RE-scores in ER^- breast cancer. These results are robust to the microRNA target prediction method. To interpret these findings, we analyzed the level of microRNA expression in previous studies and found that higher microRNA expression was not always accompanied by higher inhibitory effects. However, several key microRNA processing genes, especially Ago2 and Dicer, were differentially expressed between ER^- and ER⁺ breast cancer, which may explain the different regulatory effects of microRNAs in these two breast cancer subtypes.

Conclusions

The RE-score is a promising indicator to measure microRNAs'' inhibitory effects. Most microRNAs exhibit higher RE-scores in ER^- than in ER⁺ samples, suggesting that they have stronger inhibitory effects in ER^- breast cancers.     

microRNA target predictions across seven Drosophila species and comparison to mammalian targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.     

Comprehensive miRNome and in silico analyses identify the Wnt signaling pathway to be altered in the diabetic liver

Aberrant microRNA expression patterns underlie the pathogenesis of diverse diseases, however in a disease as complex as diabetes where the liver exhibits deregulations of normal metabolic processes, the status and role of microRNAs are not yet completely understood. In a step towards unraveling this correlation, we assessed the global microRNA expression profiles in the control and diabetic (db/db) mice liver. These db/db mice were on a C57BLKS/J background and they exhibit diabetic phenotypes that are remarkably similar to those in humans. microRNA microarray profiling revealed 11 miRNAs to be up-regulated and 2 to be down-regulated in the db/db mice liver. Predicted targets of these differentially expressed microRNAs were retrieved from miRanda and TargetScan and the maximum number of commonly predicted targets mapped onto the Wnt signaling pathway that is otherwise conventionally associated with organogenesis and development. Towards validation of this prediction, we found that major components of the Wnt signaling pathway are inhibited in the db/db mice liver. A significant number of these down-regulated genes of the Wnt signaling pathway are predicted targets to the up-regulated miRNAs and specifically our results show that miR-34a and miR-22 decreased the protein levels of their targets. Overexpression of miR-34a and miR-22 and also inhibition of Wnt signaling using specific inhibitors led to increased lipid accumulation in HepG2 cells. Our data suggest that the Wnt signaling pathway could contribute towards the deregulated hepatic behavior in these animals and an altered hepatic miRNA signature could be playing a regulatory role herein.     

DNA Methylation Landscapes of Human Fetal Development

Remodelling the methylome is a hallmark of mammalian development and cell differentiation. However, current knowledge of DNA methylation dynamics in human tissue specification and organ development largely stems from the extrapolation of studies in vitro and animal models. Here, we report on the DNA methylation landscape using the 450k array of four human tissues (amnion, muscle, adrenal and pancreas) during the first and second trimester of gestation (9,18 and 22 weeks). We show that a tissue-specific signature, constituted by tissue-specific hypomethylated CpG sites, was already present at 9 weeks of gestation (W9). Furthermore, we report large-scale remodelling of DNA methylation from W9 to W22. Gain of DNA methylation preferentially occurred near genes involved in general developmental processes, whereas loss of DNA methylation mapped to genes with tissue-specific functions. Dynamic DNA methylation was associated with enhancers, but not promoters. Comparison of our data with external fetal adrenal, brain and liver revealed striking similarities in the trajectory of DNA methylation during fetal development. The analysis of gene expression data indicated that dynamic DNA methylation was associated with the progressive repression of developmental programs and the activation of genes involved in tissue-specific processes. The DNA methylation landscape of human fetal development provides insight into regulatory elements that guide tissue specification and lead to organ functionality.