No abstract

DNA methylation and copy number in the genomes of three immortalized prostate epithelial and five cancer cell lines (LNCaP, PC3, PC3M, PC3M-Pro4, and PC3M-LN4) were compared using a microarray-based technique. Genomic DNA is cut with a methylation-sensitive enzyme HpaII, followed by linker ligation, polymerase chain reaction (PCR) amplification, labeling, and hybridization to an array of promoter sequences. Only those parts of the genomic DNA that have unmethylated restriction sites within a few hundred base pairs generate PCR products detectable on an array. Of 2732 promoter sequences on a test array, 504 (18.5%) showed differential hybridization between immortalized prostate epithelial and cancer cell lines. Among candidate hypermethylated genes in cancer-derived lines, there were eight (CD44, CDKN1A, ESR1, PLAU, RARB, SFN, TNFRSF6, and TSPY) previously observed in prostate cancer and 13 previously known methylation targets in other cancers (ARHI, bcl-2, BRCA1, CDKN2C, GADD45A, MTAP, PGR, SLC26A4, SPARC, SYK, TJP2, UCHL1, and WIT-1). The majority of genes that appear to be both differentially methylated and differentially regulated between prostate epithelial and cancer cell lines are novel methylation targets, including PAK6, RAD50, TLX3, PIR51, MAP2K5, INSR, FBN1, and GG2-1, representing a rich new source of candidate genes used to study the role of DNA methylation in prostate tumors.

Identification of gene expression changes that improve prediction of survival time across all glioma grades would be clinically useful. Four Affymetrix GeneChip datasets from the literature, containing data from 771 glioma samples representing all WHO grades and eight normal brain samples, were used in an ANOVA model to screen for transcript changes that correlated with grade. Observations were confirmed and extended using qPCR assays on RNA derived from 38 additional glioma samples and eight normal samples for which survival data were available. RNA levels of eight major mitotic spindle assembly checkpoint (SAC) genes (BUB1, BUB1B, BUB3, CENPE, MAD1L1, MAD2L1, CDC20, TTK) significantly correlated with glioma grade and six also significantly correlated with survival time. In particular, the level of BUB1B expression was highly correlated with survival time (p<0.0001), and significantly outperformed all other measured parameters, including two standards; WHO grade and MIB-1 (Ki-67) labeling index. Measurement of the expression levels of a small set of SAC genes may complement histological grade and other clinical parameters for predicting survival time.

The RNA-binding protein AUF1 binds AU-rich elements in 3′-untranslated regions to regulate mRNA degradation and/or translation. Many of these mRNAs are predicted microRNA targets as well. An emerging theme in post-transcriptional control of gene expression is that RNA-binding proteins and microRNAs co-regulate mRNAs. Recent experiments and bioinformatic analyses suggest this type of co-regulation may be widespread across the transcriptome. Here, we identified mRNA targets of AUF1 from a complex pool of cellular mRNAs and examined a subset of these mRNAs to explore the links between RNA binding and mRNA degradation for both AUF1 and Argonaute 2 (AGO2), which is an essential effector of microRNA-induced gene silencing. Depending on the specific mRNA examined, AUF1 and AGO2 binding is proportional/cooperative, reciprocal/competitive or independent. For most mRNAs in which AUF1 affects their decay rates, mRNA degradation requires AGO2. Thus, AUF1 and AGO2 present mRNA-specific allosteric binding relationships for co-regulation of mRNA degradation.