Background:Formalin-fixed, paraffin-embedded (FFPE) tumour tissue represents an immense but mainly untapped resource with respect to molecular profiling. The DASL (cDNA-mediated Annealing, Selection, extension, and Ligation) assay is a recently described, RT–PCR-based, highly multiplexed high-throughput gene expression platform developed by Illumina specifically for fragmented RNA typically obtained from FFPE specimens, which enables expression profiling. In order to extend the utility of the DASL assay for breast cancer, we have custom designed and validated a 512-gene human breast cancer panel.Methods:The RNA from FFPE breast tumour specimens were analysed using the DASL assay. Breast cancer subtype was defined from pathology immunohistochemical (IHC) staining. Differentially expressed genes between the IHC-defined subtypes were assessed by prediction analysis of microarrays (PAM) and then used in the analysis of two published data sets with clinical outcome data.Results:Gene expression signatures on our custom breast cancer panel were very reproducible between replicates (average Pearson's R2=0.962) and the 152 genes common to both the standard cancer DASL panel (Illumina) and our breast cancer DASL panel were similarly expressed for samples run on both panels (average R2=0.877). Moreover, expression of ESR1, PGR and ERBB2 corresponded well with their respective pathology-defined IHC status. A 30-gene set indicative of IHC-defined breast cancer subtypes was found to segregate samples based on their subtype in our data sets and published data sets. Furthermore, several of these genes were significantly associated with overall survival (OS) and relapse-free survival (RFS) in these previously published data sets, indicating that they are biomarkers of the different breast cancer subtypes and the prognostic outcomes associated with these subtypes.Conclusion:We have demonstrated the ability to expression profile degraded RNA transcripts derived from FFPE tissues on the DASL platform. Importantly, we have identified a 30-biomarker gene set that can classify breast cancer into subtypes and have shown that a subset of these markers is prognostic of OS and RFS.

ABSTRACT. The aim of this study was to identify feature genes that are associated with hereditary hemochromatosis (HHC; iron overload) in cardiac and skeletal muscle of mice. First, the expression profile GSE9726 was downloaded from Gene Expression Omnibus database which included 12 samples. Then the differentially expressed genes (DEGs) were identified by R language. Furthermore, the KUPS software was used to identify relationships in interactions among common DEGs in the cardiac and skeletal muscles. We then used the EASE software to obtain enriched pathways in a gene interaction network. Finally, we used the plugins of the Cytoscape software, i.e., Mcode and Bingo, to conduct module analysis. By comparing diseased and normal tissue samples, 5 and 6 genes in the cardiac and skeletal muscles, respectively, were identified as DEGs. We observed that the S100a8 and S100a9 genes were common DEGs in both tissues examined. In addition, we constructed an interaction network with common DEGs and their interacting components, and identified S100a8 and S100a9 as being associated with immune responses. In view of the relationship between the early stage of myelodysplastic syndrome and the immune system, we hypothesize that 6241 ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 12 (4): 6240-6248 (2013) Identifying genes in hereditary hemochromatosis the expression of the S100a8 and S100a9 genes is a feature that can be used for diagnosis during the early stage of the myelodysplastic syndrome and that the 2 genes could be used as targets in treating this disease.