We
computationally determined miRs that are significantly connected to molecular
pathways by utilizing gene expression profiles in different cancer types such
as glioblastomas, ovarian and breast cancers. Specifically, we assumed that the
knowledge of physical interactions between miRs and genes indicated subsets of
important miRs (IM) that significantly contributed to the regression of
pathway-specific enrichment scores. Despite the different nature of the
considered cancer types, we found strongly overlapping sets of IMs.
Furthermore, IMs that were important for many pathways were enriched with
literature-curated cancer and differentially expressed miRs. Such sets of IMs
also coincided well with clusters of miRs that were experimentally indicated in
numerous other cancer types. In particular, we focused on an overlapping set of
99 overall important miRs (OIM) that were found in glioblastomas, ovarian and
breast cancers simultaneously. Notably, we observed that interactions between
OIMs and leading edge genes of differentially expressed pathways were
characterized by considerable changes in their expression correlations. Such
gains/losses of miR and gene expression correlation indicated miR/gene pairs
that may play a causal role in the underlying cancers.
Source: Important
miRs of Pathways in Different Tumor Types. Wuchty S (wuchtys@ncbi.nlm.nih.gov),
Arjona D, Bauer PO. PLoS
Comput Biol. 2013 Jan;9(1):e1002883.
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