Identifying Tightly Regulated and Variably Expressed Networks by Differential Rank Conservation (DIRAC)

A powerful way to separate signal from noise in biology is to convert the molecular data from individual genes or proteins into an analysis of comparative biological network behaviors. One of the limitations of previous network analyses is that they do not take into account the combinatorial nature of gene interactions within the network. We report here a new technique, Differential Rank Conservation (DIRAC), which permits one to assess these combinatorial interactions to quantify various biological pathways or networks in a comparative sense, and to determine how they change in different individuals experiencing the same disease process. This approach is based on the relative expression values of participating genes—i.e., the ordering of expression within network profiles. DIRAC provides quantitative measures of how network rankings differ either among networks for a selected phenotype or among phenotypes for a selected network. We examined disease phenotypes including cancer subtypes and neurological disorders and identified networks that are tightly regulated, as defined by high conservation of transcript ordering. Interestingly, we observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. At a sample level, DIRAC can detect a change in ranking between phenotypes for any selected network. Variably expressed networks represent statistically robust differences between disease states and serve as signatures for accurate molecular classification, validating the information about expression patterns captured by DIRAC. Importantly, DIRAC can be applied not only to transcriptomic data, but to any ordinal data type

Recent Advancees in Cancer Treatment

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Androgen receptors are acquired by healthy postmenopausal endometrial epithelium and their subsequent loss in endometrial cancer is associated with poor survival

background: Endometrial cancer (EC) is a hormone-driven disease, and androgen receptor (AR) expression in high-grade EC (HGEC) and metastatic EC has not yet been described. methods: The expression pattern and prognostic value of AR in relation to oestrogen (ERα and ERβ) and progesterone (PR) receptors, and the proliferation marker Ki67 in all EC subtypes (n=85) were compared with that of healthy and hyperplastic endometrium, using immunohistochemisty and qPCR. results: Compared with proliferative endometrium, postmenopausal endometrtial epithelium showed significantly higher expression of AR (P<0.001) and ERα (P=0.035), which persisted in hyperplastic epithelium and in low-grade EC (LGEC). High-grade EC showed a significant loss of AR (P<0.0001), PR (P<0.0001) and ERβ (P<0.035) compared with LGEC, whilst maintaining weak to moderate ERα. Unlike PR, AR expression in metastatic lesions was significantly (P=0.039) higher than that in primary tumours. Androgen receptor expression correlated with favourable clinicopathological features and a lower proliferation index. Loss of AR, with/without the loss of PR was associated with a significantly lower disease-free survival (P<0.0001, P<0.0001, respectively). conclusions: Postmenopausal endometrial epithelium acquires AR whilst preserving other steroid hormone receptors. Loss of AR, PR with retention of ERα and ERβ may promote the unrestrained growth of HGEC. Androgen receptor may therefore be a clinically relevant prognostic indicator and a potential therapeutic target in EC