miRNA-mediated functional changes through co-regulating function related genes

Background

microRNAs play important roles in various biological processes involving fairly complex mechanism. Analysis of genome-wide miRNA microarray demonstrate that a single miRNA can regulate hundreds of genes, but the regulative extent on most individual genes is surprisingly mild so that it is difficult to understand how a miRNA provokes detectable functional changes with such mild regulation.

Results

To explore the internal mechanism of miRNA-mediated regulation, we re-analyzed the data collected from genome-wide miRNA microarray with bioinformatics assay, and found that the transfection of miR-181b and miR-34a in Hela and HCT-116 tumor cells regulated large numbers of genes, among which, the genes related to cell growth and cell death demonstrated high Enrichment scores, suggesting that these miRNAs may be important in cell growth and cell death. MiR-181b induced changes in protein expression of most genes that were seemingly related to enhancing cell growth and decreasing cell death, while miR-34a mediated contrary changes of gene expression. Cell growth assays further confirmed this finding. In further study on miR-20b-mediated osteogenesis in hMSCs, miR-20b was found to enhance osteogenesis by activating BMPs/Runx2 signaling pathway in several stages by co-repressing of PPARγ, Bambi and Crim1.

Conclusions

With its multi-target characteristics, miR-181b, miR-34a and miR-20b provoked detectable functional changes by co-regulating functionally-related gene groups or several genes in the same signaling pathway, and thus mild regulation from individual miRNA targeting genes could have contributed to an additive effect. This might also be one of the modes of miRNA-mediated gene regulation.     

GWAS Meets Microarray: Are the Results of Genome-Wide Association Studies and Gene-Expression Profiling Consistent? Prostate Cancer as an Example

Background

Genome-wide association studies (GWASs) and global profiling of gene expression (microarrays) are two major technological breakthroughs that allow hypothesis-free identification of candidate genes associated with tumorigenesis. It is not obvious whether there is a consistency between the candidate genes identified by GWAS (GWAS genes) and those identified by profiling gene expression (microarray genes).

Methodology/Principal Findings

We used the Cancer Genetic Markers Susceptibility database to retrieve single nucleotide polymorphisms from candidate genes for prostate cancer. In addition, we conducted a large meta-analysis of gene expression data in normal prostate and prostate tumor tissue. We identified 13,905 genes that were interrogated by both GWASs and microarrays. On the basis of P values from GWASs, we selected 1,649 most significantly associated genes for functional annotation by the Database for Annotation, Visualization and Integrated Discovery. We also conducted functional annotation analysis using same number of the top genes identified in the meta-analysis of the gene expression data. We found that genes involved in cell adhesion were overrepresented among both the GWAS and microarray genes.

Conclusions/Significance

We conclude that the results of these analyses suggest that combining GWAS and microarray data would be a more effective approach than analyzing individual datasets and can help to refine the identification of candidate genes and functions associated with tumor development.     

Predictive genes in adjacent normal tissue are preferentially altered by sCNV during tumorigenesis in liver cancer and may rate limiting

Background

In hepatocellular carcinoma (HCC) genes predictive of survival have been found in both adjacent normal (AN) and tumor (TU) tissues. The relationships between these two sets of predictive genes and the general process of tumorigenesis and disease progression remains unclear.

Methodology/Principal Findings

Here we have investigated HCC tumorigenesis by comparing gene expression, DNA copy number variation and survival using ∼250 AN and TU samples representing, respectively, the pre-cancer state, and the result of tumorigenesis. Genes that participate in tumorigenesis were defined using a gene-gene correlation meta-analysis procedure that compared AN versus TU tissues. Genes predictive of survival in AN (AN-survival genes) were found to be enriched in the differential gene-gene correlation gene set indicating that they directly participate in the process of tumorigenesis. Additionally the AN-survival genes were mostly not predictive after tumorigenesis in TU tissue and this transition was associated with and could largely be explained by the effect of somatic DNA copy number variation (sCNV) in cis and in trans. The data was consistent with the variance of AN-survival genes being rate-limiting steps in tumorigenesis and this was confirmed using a treatment that promotes HCC tumorigenesis that selectively altered AN-survival genes and genes differentially correlated between AN and TU.

Conclusions/Significance

This suggests that the process of tumor evolution involves rate-limiting steps related to the background from which the tumor evolved where these were frequently predictive of clinical outcome. Additionally treatments that alter the likelihood of tumorigenesis occurring may act by altering AN-survival genes, suggesting that the process can be manipulated. Further sCNV explains a substantial fraction of tumor specific expression and may therefore be a causal driver of tumor evolution in HCC and perhaps many solid tumor types.     

Reordering hierarchical tree based on bilateral symmetric distance

Background

In microarray data analysis, hierarchical clustering (HC) is often used to group samples or genes according to their gene expression profiles to study their associations. In a typical HC, nested clustering structures can be quickly identified in a tree. The relationship between objects is lost, however, because clusters rather than individual objects are compared. This results in a tree that is hard to interpret.

Methodology/Principal Findings

This study proposes an ordering method, HC-SYM, which minimizes bilateral symmetric distance of two adjacent clusters in a tree so that similar objects in the clusters are located in the cluster boundaries. The performance of HC-SYM was evaluated by both supervised and unsupervised approaches and compared favourably with other ordering methods.

Conclusions/Significance

The intuitive relationship between objects and flexibility of the HC-SYM method can be very helpful in the exploratory analysis of not only microarray data but also similar high-dimensional data.