The PTEN tumor-suppressor gene is regulated by the PTENpg1 long noncoding RNA. A new study investigating a previously uncharacterized PTENpg1-encoded antisense RNA (asRNA) now reveals that the asRNA α isoform functions as a negative regulator of PTEN transcription by recruiting chromatin remodelers and catalyzing H3K27me3 formation, whereas the asRNA β isoform interacts with PTENpg1, thereby affecting PTEN levels post-transcriptionally. PTEN is a tumor-suppressor gene that has been shown to be under the regulatory control of a PTEN pseudogene expressed noncoding RNA, PTENpg1. Here, we characterize a previously unidentified PTENpg1-encoded antisense RNA (asRNA), which regulates PTEN transcription and PTEN mRNA stability. We find two PTENpg1 asRNA isoforms, α and β. The α isoform functions in trans, localizes to the PTEN promoter and epigenetically modulates PTEN transcription by the recruitment of DNA methyltransferase 3a and Enhancer of Zeste. In contrast, the β isoform interacts with PTENpg1 through an RNA-RNA pairing interaction, which affects PTEN protein output through changes of PTENpg1 stability and microRNA sponge activity. Disruption of this asRNA-regulated network induces cell-cycle arrest and sensitizes cells to doxorubicin, which suggests a biological function for the respective PTENpg1 expressed asRNAs.

Recent studies suggest that knowledge of differential expression of microRNAs (miRNA) in cancer may have substantial diagnostic and prognostic value. Here, we use a direct sequencing method to characterize the profiles of miRNAs and other small RNA segments for six human cervical carcinoma cell lines and five normal cervical samples. Of 166 miRNAs expressed in normal cervix and cancer cell lines, we observed significant expression variation of six miRNAs between the two groups. To further show the biological relevance of our findings, we examined the expression level of two significantly varying miRNAs in a panel of 29 matched pairs of human cervical cancer and normal cervical samples. Reduced expression of miR-143 and increased expression of miR-21 were reproducibly displayed in cancer samples, suggesting the potential value of these miRNAs as tumor markers. In addition to the known miRNAs, we found a number of novel miRNAs and an additional set of small RNAs that do not meet miRNA criteria. [Cancer Res 2007;67(13):6031–43]

Malignant cutaneous melanoma is a highly aggressive form of skin cancer. Despite improvements in early melanoma diagnosis, the 5-year survival rate remains low in advanced disease. Therefore, novel biomarkers are urgently needed to devise new means of detection and treatment. In this study, we aimed to improve our understanding of microRNA (miRNA) deregulation in melanoma development and their impact on patient survival. Global miRNA expression profiles of a set of melanoma lymph node metastases, melanoma cell lines, and melanocyte cultures were determined using Agilent array. Deregulated miRNAs were evaluated in relation with clinical characteristics, patient survival, and mutational status for BRAF and NRAS. Several miRNAs were differentially expressed between melanocytes and melanomas as well as melanoma cell lines. In melanomas, miR-193a, miR-338, and miR-565 were underexpressed in cases with a BRAF mutation. Furthermore, low expression of miR-191 and high expression of miR-193b were associated with poor melanoma-specific survival. In conclusion, our findings show miRNA dysregulation in malignant melanoma and its relation to established molecular backgrounds of BRAF and NRAS oncogenic mutations. The identification of an miRNA classifier for poor survival may lead to the development of miRNA detection as a complementary prognostic tool in clinical practice.

MicroRNAs (miRNAs) are ∼22 nucleotide-long noncoding RNAs involved in several biological processes including development, differentiation and proliferation. Recent studies suggest that knowledge of miRNA expression patterns in cancer may have substantial value for diagnostic and prognostic determinations as well as for eventual therapeutic intervention. We performed comprehensive analysis of miRNA expression profiles of 27 sarcomas, 5 normal smooth muscle and 2 normal skeletal muscle tissues using microarray technology and/or small RNA cloning approaches. The miRNA expression profiles are distinct among the tumor types as demonstrated by an unsupervised hierarchical clustering, and unique miRNA expression signatures were identified in each tumor class. Remarkably, the miRNA expression patterns suggested that two of the sarcomas had been misdiagnosed and this was confirmed by reevaluation of the tumors using histopathologic and molecular analyses. Using the cloning approach, we also identified 31 novel miRNAs or other small RNA effectors in the sarcomas and normal skeletal muscle tissues examined. Our data show that different histological types of sarcoma have distinct miRNA expression patterns, reflecting the apparent lineage and differentiation status of the tumors. The identification of unique miRNA signatures in each tumor type may indicate their role in tumorigenesis and may aid in diagnosis of soft tissue sarcomas.

Background: Ultra-high throughput sequencing technologies provide opportunities both for discovery of novel molecular species and for detailed comparisons of gene expression patterns. Small RNA populations are particularly well suited to this analysis, as many different small RNAs can be completely sequenced in a single instrument run. Results: We prepared small RNA libraries from 29 tumour/normal pairs of human cervical tissue samples. Analysis of the resulting sequences (42 million in total) defined 64 new human microRNA (miRNA) genes. Both arms of the hairpin precursor were observed in twenty-three of the newly identified miRNA candidates. We tested several computational approaches for the analysis of class differences between high throughput sequencing datasets and describe a novel application of a log linear model that has provided the most effective analysis for this data. This method resulted in the identification of 67 miRNAs that were differentially-expressed between the tumour and normal samples at a false discovery rate less than 0.001. Conclusions: This approach can potentially be applied to any kind of RNA sequencing data for analysing differential sequence representation between biological sample sets.