Toward the blood-borne miRNome of human diseases.

In a multicenter study, we determined the expression profiles of 863 microRNAs by array analysis of 454 blood samples from human individuals with different cancers or noncancer diseases, and validated this 'miRNome' by quantitative real-time PCR. We detected consistently deregulated profiles for all tested diseases; pathway analysis confirmed disease association of the respective microRNAs. We observed significant correlations (P = 0.004) between the genomic location of disease-associated genetic variants and deregulated microRNAs.

Stable serum miRNA profiles as potential tool for non-invasive lung cancer diagnosis.

Circulating microRNAs in human serum have increasingly been recognized as stable markers for cancer detection. However, there is still a lack of miRNome wide studies over a long period of time with respect to pathogenic processes. We obtained serum samples from the janus serum bank collected prior and after diagnosis of lung cancer. We analyzed the abundance of 904 miRNAs in serum from eight cancer patients at three time points and from six healthy control individuals. Based on the identified miRNA signatures, hierarchical clustering and a self-organizing map identified three major clusters including one cluster containing most of the of the pre-diagnostic samples, a second cluster with mainly post-diagnostic samples, and a third cluster with mainly control samples. Correlation analyses showed that although the profiles were generally stable over several years, most obvious changes of the miRNA pattern seem to occur at a time close to diagnosis. Our findings support the idea that a developing lung cancer might be detectable years prior to diagnosis through a specific miRNA signature and that this signature changes during tumor development.

Tests of rRNA hybridization to microarrays suggest that hybridization characteristics of oligonucleotide probes for species discrimination cannot be predicted.

Hybridization of rRNAs to microarrays is a promising approach for prokaryotic and eukaryotic species identification. Typically, the amount of bound target is measured by fluorescent intensity and it is assumed that the signal intensity is directly related to the target concentration. Using thirteen different eukaryotic LSU rRNA target sequences and 7693 short perfect match oligonucleotide probes, we have assessed current approaches for predicting signal intensities by comparing Gibbs free energy (DeltaG degrees) calculations to experimental results. Our evaluation revealed a poor statistical relationship between predicted and actual intensities. Although signal intensities for a given target varied up to 70-fold, none of the predictors were able to fully explain this variation. Also, no combination of different free energy terms, as assessed by principal component and neural network analyses, provided a reliable predictor of hybridization efficiency. We also examined the effects of single-base pair mismatch (MM) (all possible types and positions) on signal intensities of duplexes. We found that the MM effects differ from those that were predicted from solution-based hybridizations. These results recommend against the application of probe design software tools that use thermodynamic parameters to assess probe quality for species identification. Our results imply that the thermodynamic properties of oligonucleotide hybridization are by far not yet understood.