Identification of novel pathway partners of p68 and p72 RNA helicases through Oncomine meta-analysis

The Oncominedatabase is an online collection of microarrays from various sources, usually cancer-related, and contains many "multi-arrays" (collections of analyzed microarrays, in a single study). As there are often many hundreds of tumour samples/microarrays within a single multi-array results from coexpressed genes can be analyzed, and are fully searchable. This gives a potentially significant list of coexpressed genes, which is important to define pathways in which the gene of interest is involved. However, to increase the likelihood of revealing truly significant coexpressed genes we have analyzed their frequency of occurrence over multiple studies (meta-analysis), greatly increasing the significance of results compared to those of a single study. We have used the DEAD-box proteins p68(Ddx5) and p72(Ddx17) as models for this coexpression frequency analysis as there are defined functions for these proteins in splicing and transcription (known functions which we could use as a basis for quality control). Furthermore, as these proteins are highly similar, interact together, and may be to some degree functionally redundant, we then analyzed the overlap between coexpressed genes of p68 and p72. This final analysis gave us a highly significant list of coexpressed genes, clustering mainly in splicing and transcription (recapitulating their published roles), but also revealing new pathways such as cytoskeleton remodelling and protein folding. We have further tested a predicted pathway partner, RNA helicase A(Dhx9) in a reciprocal meta-analysis that identified p68 and p72 as being coexpressed, and further show a direct interaction of Dhx9 with p68 and p72, attesting to the predictive nature of this technique. In summary we have extended the capabilities of Oncomineby analyzing the frequency of coexpressed genes over multiple studies, and furthermore assessing the overlap with a known pathway partner (in this case p68 with p72). We have shown our predictions corroborate previously published studies on p68 and p72, and that novel predictions can be easily tested. These techniques are widely applicable and should increase the quality of data from future meta-analysis studies.