Calaluce et al. BMC Cancer 2010, 10:126 http://www.biomedcentral.com/1471-2407/10/126 Posttranscriptional gene regulation is increasingly being appreciated as a driver of malignant transforma- tion. The roles of both RBPs and miRNAs (so-called oncomirs) are being recognized in cancer [71]. Many reports have described alterations in miRNA expression profile and function as contributing to breast cancer malignant transformation and metastasis [72-75]. HuR RIP-Chip analysis may shed further light into malignant breast cancer transformation by identifying HuR asso- ciated mRNAs. We believe that there are potential applications for tamoxifen resistance as well. Keen and colleagues have described a potential mechanistic link between HuR expression and tamoxifen drug resistance [76]. As breast cancer cells acquire tamoxifen resistance, there are increased levels of cytoplasmic HuR expression. Increased cytoplasmic HuR levels have previously been described in situations where HuR actively influences expression of cytoplasmic targets [18,47,48]. Drug resis- tance could be reversed by using siRNA to knock-down HuR expression, whereas exogenous over-expression of HuR could cause cells to become resistant to tamoxifen. We therefore propose that HuR may be coordinately regulating genes which may allow a cell to acquire tamoxifen resistance. It will be interesting to further investigate HuR-associated target genes in ER+ cells in this light. Conclusion In summary, using RIP-Chip analysis, we have per- formed for the first time a genome-wide comparison of HuR-associated targets in wild type ER+ and ER- breast cancer. We have identified novel HuR targets and have gained insight into the role HuR plays in regulating known cancer genes. We found distinct, differentially expressed subsets of HuR cancer related genes in ER+ and ER- breast cancer cell lines. Based on our observa- tions, the enhanced expression of these mRNA subsets by HuR can influence many of the acquired capabilities of cancer cells. Further investigation into HuR’s role in regulating these genes may provide novel insights into breast cancer diagnosis and therapy. Appendix Microarray Data Preprocessing Data quality was examined by looking at quality controls metrics produced by Illumina’s software (BeadStudio v3.1.3.0, Gene Expression Module 3.2.7). The data were then exported for further analyses in R. Image plots of each array were examined for spatial artifacts, and there was no evidence of systematic effects indicative of tech- nical problems with the arrays. Within limma, quantile normalization was used for between chip normalization. Finally, quality control statistics were computed using a Page 13 of 15 variety of Illumina’s internal control probes that are replicated on each array. Any probes which were consid- ered “not detectable” across all samples were excluded from further statistical analyses in order to reduce false positives. The determination of “not detectable” was based upon the BeadStudio computed detection p-value being greater than 1%. Gene Ontology Gene Universe In defining the gene universe for the analysis, non-speci- fic filtering was used to increase statistical power with- out biasing the results. We started with all probes on the Illumina array which had both an Entrez gene iden- tifier [77] and a GO annotation, as provided in the lumi- HumanAll.db [78] annotation data package and GO.db [79] annotation maps (built using data obtained from NCBI on 4/2/08). This set was then reduced by exclud- ing probes that exhibited little variability (interquartile range (IQR) of <0.1 on log2 scale) across all samples because such probes are generally not informative. Finally, for probes that mapped to the same Entrez iden- tifier, a single probe was chosen in order to insure a surjective map from probe IDs to GO categories (via Entrez identifiers). This was necessary to avoid redun- dantly counting GO categories which produces false positives. Probes with the largest IQR were chosen to be associated with an Entrez identifier. Additional file 1: Figure S2. Table of complete GO analysis. Listing of HuR-associated genes with odds ratios and functional categories. Additional file 2: Table S1. HuR targets five fold or greater. Listing of HuR-associated mRNAs in MB-231 and MCF-7 cell lines. Additional file 3: Figure S3. Total cellular levels of HuR are similar in MB-231 and MCF-7 cells. Nuclear and cytoplasmic separation was performed to measure levels of HuR in different compartments of MB- 231 and MCF-7 cells. Total cellular HuR levels were very similar, whereas there was a small (10%) increase in HuR cytoplasmic levels in MB-231 cells as compared to MCF-7. Absence of b-tubulin staining demonstrates integrity of isolation as there should not be b-tubulin in the nuclear fraction. Bands were measured by densitometry and normalized to b- tubulin controls. (T = total cellular lysate; C = cytoplasmic lysate, N = nuclear lysate). Additional file 4: Figure S1. Relative baseline values of CALM1, CALM2, CALM3, and CD-9 mRNAs in ER+ and ER- cells. Quantitative RT-PCR performed on mRNA extracted from cell lysates showing relative levels of CALM1, CALM2, CALM3, and CD-9 mRNAs in MB-231 and MCF-7 breast cancer cells. All values were normalized to GAPDH mRNA. All experiments were done in triplicate (n = 3) except for CALM3 (n = 2). Abbreviations (ER-): Estrogen receptor negative; (ER+): estrogen receptor positive; (RIP): RNA immunoprecipitation; (RIP-Chip): RNA immunoprecipitation applied to microarrays; (3’ UTR): 3’ untranslated region; ELAV1: (embryonic lethal abnormal vision 1). Acknowledgements YK and MG were supported by the NIA-IRP, NIH. UA and JWD were supported by Department of Defense (Idea Award W81XWH-07-1-0406) as