Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

Background: Archival formalin-fixed paraffin-embedded (FFPE) tissues have limited utility in applications involving analysis of gene expression due to mRNA degradation and modification during fixation and processing. This study analyzed 160 miRNAs in paired snap frozen and FFPE cells to investigate if miRNAs may be successfully detected in archival specimens.

IntroductionBreast cancer is a complex heterogeneous disease for which a substantial resource of transcriptomic data is available. Gene expression data have facilitated the division of breast cancer into, at least, five molecular subtypes, namely luminal A, luminal B, HER2, normal-like and basal. Once identified, breast cancer subtypes can inform clinical decisions surrounding patient treatment and prognosis. Indeed, it is important to identify patients at risk of developing aggressive disease so as to tailor the level of clinical intervention.MethodsWe have developed a user-friendly, web-based system to allow the evaluation of genes/microRNAs (miRNAs) that are significantly associated with survival in breast cancer and its molecular subtypes. The algorithm combines gene expression data from multiple microarray experiments which frequently also contain miRNA expression information, and detailed clinical data to correlate outcome with gene/miRNA expression levels. This algorithm integrates gene expression and survival data from 26 datasets on 12 different microarray platforms corresponding to approximately 17,000 genes in up to 4,738 samples. In addition, the prognostic potential of 341 miRNAs can be analysed.ResultsWe demonstrated the robustness of our approach in comparison to two commercially available prognostic tests, oncotype DX and MammaPrint. Our algorithm complements these prognostic tests and is consistent with their findings. In addition, BreastMark can act as a powerful reductionist approach to these more complex gene signatures, eliminating superfluous genes, potentially reducing the cost and complexity of these multi-index assays. Known miRNA prognostic markers, mir-205 and mir-93, were used to confirm the prognostic value of this tool in a miRNA setting. We also applied the algorithm to examine expression of 58 receptor tyrosine kinases in the basal-like subtype, identifying six receptor tyrosine kinases associated with poor disease-free survival and/or overall survival (EPHA5, FGFR1, FGFR3, VEGFR1, PDGFRβ, and TIE1). A web application for using this algorithm is currently available.ConclusionsBreastMark is a powerful tool for examining putative gene/miRNA prognostic markers in breast cancer. The value of this tool will be in the preliminary assessment of putative biomarkers in breast cancer. It will be of particular use to research groups with limited bioinformatics facilities.

MicroRNAs are a group of small non-coding RNAs approximately 22 nucleotides in length. Recent work has shown differential expression of mature microRNAs in human cancers. Production and function of microRNAs require coordinated processing by proteins of the microRNA machinery. Dicer and Drosha (RNase III endonucleases) are essential components of the microRNA machinery. Recently, the ribosome anti-association factor eIF6 has also been found to have a role in microRNA-mediated post-transcriptional silencing. We characterized the alterations in the expression of genes encoding proteins of microRNA machinery in ovarian serous carcinoma. Protein expression of eIF6 and Dicer was quantified in a tissue microarray of 66 ovarian serous carcinomas. Dicer, Drosha and eIF6 mRNA expression was analysed using quantitative reverse transcription-PCR on an independent set of 50 formalin-fixed, paraffin-embedded ovarian serous carcinoma samples. Expression profiles of eIF6 and Dicer were correlated with clinicopathological and patient survival data. We provide definitive evidence that eIF6 and Dicer are both upregulated in a significant proportion of ovarian serous carcinomas and are associated with specific clinicopathological features, most notably low eIF6 expression being associated with reduced disease-free survival. The status of eIF6 and proteins of the microRNA machinery may help predict toxicity and susceptibility to future interfering RNA-based therapy.