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Microarray-based gene expression profiling is well suited for parallel quantitative analysis of large numbers of RNAs, but its application to cancer biopsies, formalin-fixed paraffin-embedded (FFPE) archived tissues in particular, is limited by the poor quality of the RNA recovered. This represents a serious drawback, since FFPE tumor tissue banks are available with clinical and prognostic annotations, which could be exploited for molecular profiling studies, provided that reliable analytical technologies are found. We applied and evaluated here a microarray-based cDNA-mediated annealing, selection, extension and ligation (DASL) assay for analysis of 502 mRNAs in highly degraded total RNA extracted from cultured cells or FFPE breast cancer biopsies. The study included quantitative and qualitative comparison of data obtained by analysis of the same RNAs with genome-wide oligonucleotide microarrays vs. DASL arrays and, by DASL, before and after extensive in vitro RNA fragmentation. The DASL-based expression profiling assay applied to RNA extracted from MCF-7 cells before or after 24 hours stimulation with a mitogenic dose of 17β-estradiol consistently allowed to detect hormone-induced gene expression changes also following extensive RNA degradation in vitro. Comparable results where obtained with tumor RNA extracted from FFPE breast cancer biopsies (6 to 19 years old). The method proved itself sensitive, reproducible and accurate, when compared with results obtained by microarray analysis of RNA extracted from snap-frozen tissue of the same tumor.

C/EBPs are a family of B-Zip transcription factors -TFs- involved in the regulation of differentiation in several tissues. The two most studied members -C/EBPα and C/EBPβ- play important roles in skin homeostasis and their ablation reveals cells with stem cells signatures. Much less is known about C/EBPδ which is highly expressed in the granular layer of interfollicular epidermis and is a direct target of p63, the master regular of multilayered epithelia. We identified C/EBPδ target genes in human primary keratinocytes by ChIP on chip and profiling of cells functionally inactivated with siRNA. Categorization suggests a role in differentiation and control of cell-cycle, particularly of G2/M genes. Among positively controlled targets are numerous genes involved in barrier function. Functional inactivation of C/EBPδ as well as overexpressions of two TF targets -MafB and SOX2- affect expression of markers of keratinocyte differentiation. We performed IHC on skin tumor tissue arrays: expression of C/EBPδ is lost in Basal Cell Carcinomas, but a majority of Squamous Cell Carcinomas showed elevated levels of the protein. Our data indicate that C/EBPδ plays a role in late stages of keratinocyte differentiation. © 2010 Borrelli et al.

Luminal-like breast tumor cells express estrogen receptor alpha (ERalpha), a member of the nuclear receptor family of ligand-activated transcription factors that controls their proliferation, survival, and functional status. To identify the molecular determinants of this hormone-responsive tumor phenotype, a comprehensive genome-wide analysis was performed in estrogen stimulated MCF-7 and ZR-75.1 cells by integrating time-course mRNA expression profiling with global mapping of genomic ERalpha binding sites by chromatin immunoprecipitation coupled to massively parallel sequencing, microRNA expression profiling, and in silico analysis of transcription units and receptor binding regions identified. All 1270 genes that were found to respond to 17beta-estradiol in both cell lines cluster in 33 highly concordant groups, each of which showed defined kinetics of RNA changes. This hormone-responsive gene set includes several direct targets of ERalpha and is organized in a gene regulation cascade, stemming from ligand-activated receptor and reaching a large number of downstream targets via AP-2gamma, B-cell activating transcription factor, E2F1 and 2, E74-like factor 3, GTF2IRD1, hairy and enhancer of split homologue-1, MYB, SMAD3, RARalpha, and RXRalpha transcription factors. MicroRNAs are also integral components of this gene regulation network because miR-107, miR-424, miR-570, miR-618, and miR-760 are regulated by 17beta-estradiol along with other microRNAs that can target a significant number of transcripts belonging to one or more estrogen-responsive gene clusters.

Background Microarray experiments enable simultaneous measurement of the expression levels of virtually all transcripts present in cells, thereby providing a ‘molecular picture’ of the cell state. On the other hand, the genomic responses to a pharmacological or hormonal stimulus are dynamic molecular processes, where time influences gene activity and expression. The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal relationships between samples. Results We compared here four different methods to analyze data derived from a time course mRNA expression profiling experiment which consisted in the study of the effects of estrogen on hormone-responsive human breast cancer cells. Gene expression was monitored with the innovative Illumina BeadArray platform, which includes an average of 30-40 replicates for each probe sequence randomly distributed on the chip surface. We present and discuss the results obtained by applying to these datasets different statistical methods for serial gene expression analysis. The influence of the normalization algorithm applied on data and of different parameter or threshold choices for the selection of differentially expressed transcripts has also been evaluated. In most cases, the selection was found fairly robust with respect to changes in parameters and type of normalization. We then identified which genes showed an expression profile significantly affected by the hormonal treatment over time. The final list of differentially expressed genes underwent cluster analysis of functional type, to identify groups of genes with similar regulation dynamics. Conclusions Several methods for processing time series gene expression data are presented, including evaluation of benefits and drawbacks of the different methods applied. The resulting protocol for data analysis was applied to characterization of the gene expression changes induced by estrogen in human breast cancer ZR-75.1 cells over an entire cell cycle.