microRNA and Cancer

Serendipity Points to New Potential Target and Therapy for Melanoma

A University of Colorado Cancer Center study in  Journal of Investigative Dermatology describes a new target and potential treatment for melanoma, the most dangerous form of skin cancer. MicroRNA can decide which genes in a cell's DNA are expressed and which stay silent. Melanoma tends to lack microRNA-26a, which makes the gene SODD go silent.

Key Role of microRNAs in Melanoma Metastasis Identified

 Researchers at the NYU Cancer Institute, an NCI-designated cancer center at NYU Langone Medical Center, identified for the first time the key role specific microRNAs (miRNAs) play in melanoma metastasis to simultaneously cause cancer cells to invade and immunosuppress the human body's ability to fight abnormal cells.

Basic Mechanism Of Skin Cancer Development Illuminated

Manuela Baccarini, Professor for Cell Signalling at the Centre of Molecular Biology of the University of Vienna (Max F. Perutz Laboratories) and colleagues reveal the function of a protein in the Ras signalling pathway. Their findings provide the basis for research on novel therapeutic strategies in Ras-induced skin cancers, e.g. melanoma. The results of her work are published in the scientific journal Cancer Cell.


 

microRNA and gene expression

Enhancer RNAs alter gene expression: New class of molecules may be key emerging 'enhancer therapy'

In a pair of distinct but complementary papers, researchers illuminate the functional importance of a relatively new class of RNA molecules. The work suggests modulation of “enhancer-directed RNAs” or “eRNAs” could provide a new way to alter gene expression in living cells, perhaps affecting the development or pathology of many diseases.

University of California, San Diego Health Sciences (2013, June 4). Enhancer RNAs alter gene expression: New class of molecules may be key emerging 'enhancer therapy'. ScienceDaily. Retrieved June 11, 2013, from http://www.sciencedaily.com­ /releases/2013/06/130604093852.htm?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+sciencedaily%2Ftop_news%2Ftop_environment+%28ScienceDaily%3A+Top+News+--+Top+Environment%29

Next-generation sequencing to identifies novel microRNAs associated with cancer conditions.

Novel microRNAs in peripheral blood of lung cancer patients:

MicroRNAs (miRNAs) are increasingly envisaged as biomarkers for various tumor and non-tumor diseases. MiRNA biomarker identification is, as of now, mostly performed in a candidate approach, limiting discovery to annotated miRNAs and ignoring unknown ones with potential diagnostic value. Scientists at Biomarker Discovery Center Heidelberg, Germany, applied high-throughput SOLiD transcriptome sequencing of miRNAs expressed in human peripheral blood of patients with lung cancer. They developed a bioinformatics pipeline to generate profiles of miRNA markers and to detect novel miRNAs with diagnostic information. Applying this approach, they detected 76 previously unknown miRNAs and 41 novel mature forms of known precursors. In addition, they identified 32 annotated and seven unknown miRNAs that were significantly altered in cancer patients. These results demonstrate that deep sequencing of small RNAs bears high potential to quantify miRNAs in peripheral blood and to identify previously unknown miRNAs serving as biomarker for lung cancer

Cancer Biol Ther. 2011 Nov 15;12(10). [Epub ahead of print]

MicroRNAs Associated with Metastatic Prostate Cancer

Metastasis is the most common cause of death of prostate cancer patients. Identification of specific metastasis biomarkers and novel therapeutic targets is considered essential for improved prognosis and management of the disease. MicroRNAs (miRNAs) form a class of non-coding small RNA molecules considered to be key regulators of gene expression. Their dysregulation has been shown to play a role in cancer onset, progression and metastasis, and miRNAs represent a promising new class of cancer biomarkers. The objective of this study was to identify down- and up-regulated miRNAs in prostate cancer that could provide potential biomarkers and/or therapeutic targets for prostate cancer metastasis.

Next generation sequencing technology was applied to identify differentially expressed miRNAs in a transplantable metastatic versus a non-metastatic prostate cancer xenograft line, both derived from one patient's primary cancer. The xenografts were developed via subrenal capsule grafting of cancer tissue into NOD/SCID mice, a methodology that tends to preserve properties of the original cancers (e.g., tumor heterogeneity, genetic profiles).Differentially expressed known miRNAs, isomiRs and 36 novel miRNAs were identified. A number of these miRNAs (21/104) have previously been reported to show similar down- or up-regulation in prostate cancers relative to normal prostate tissue, and some of them (e.g., miR-16, miR-34a, miR-126*, miR-145, miR-205) have been linked to prostate cancer metastasis, supporting the validity of the analytical approach.PLoS One. 2011;6(9):e24950. Epub 2011 Sep 30.

 Next-generation sequencing of microRNAs for breast cancer detection.

 

It is reported that different microRNA (miRNA) profiles can be detected in the blood of cancer patients. We investigated that whether the key serum miRNAs could discriminate patients with and without breast cancer. This study was divided into three parts: (1) miRNA marker discovery using SOLiD sequencing-based miRNA profiling on cancerous and adjacent noncancerous breast tissue of one breast cancer patient; (2) marker selection and validation by real-time PCR on a small set of serum; (3) gene ontology analysis of the key miRNA target genes. Of genome-wide tissue miRNA expression analysis, five miRNAs were found to be altered more than fivefold by SOLiD sequencing (i.e., miR-29a, miR-23a, miR-23b, miR-192, and miR-21). All the five miRNAs were validated on the 20 breast cancer patients and 20 controls. miR-29a and miR-21 were significantly increased in the serum of breast cancer patients (P < .05). Gene ontology analysis of the target genes revealed enrichment for special biological process categories, that is, signal transduction, development, apoptosis, cell proliferation, and cell adhesion. SOLiD sequencing provides a promising method for cancer-related miRNA profiling. Serum miRNAs may be useful biomarkers for breast cancer detection.

J Biomed Biotechnol. 2011;2011:597145. Epub 2011 May 26.

One of most improtant breakthrought of cell biology in last decade

The stem-loop RT-PCR

Plant microRNAs (miRNAs) are a class of endogenous small RNAs that are essential for plant development and survival. They arise from larger precursor RNAs with a characteristic hairpin structure and regulate gene activity by targeting mRNA transcripts for cleavage or translational repression. Efficient and reliable detection and quantification of miRNA expression has become an essential step in understanding their specific roles. The expression levels of miRNAs can vary dramatically between samples and they often escape detection by conventional technologies such as cloning, northern hybridization and microarray analysis. The stem-loop RT-PCR method described here is designed to detect and quantify mature miRNAs in a fast, specific, accurate and reliable manner. First, a miRNA-specific stem-loop RT primer is hybridized to the miRNA and then reverse transcribed. Next, the RT product is amplified and monitored in real time using a miRNA-specific forward primer and the universal reverse primer. This method enables miRNA expression profiling from as little as 10 pg of total RNA and is suitable for high-throughput miRNA expression analysis.

Varkonyi-Gasic E, Hellens RP.Quantitative Stem-Loop RT-PCR for Detection of MicroRNAs. Methods Mol Biol. 2011;744:145-57.

A novel microRNA (miRNA) quantification method has been developed using stem-loop RT followed by TaqMan PCR analysis. Stem-loop RT primers are better than conventional ones in terms of RT efficiency and specificity. TaqMan miRNA assays are specific for mature miRNAs and discriminate among related miRNAs that differ by as little as one nucleotide. Furthermore, they are not affected by genomic DNA contamination. Precise quantification is achieved routinely with as little as 25 pg of total RNA for most miRNAs. In fact, the high sensitivity, specificity and precision of this method allows for direct analysis of a single cell without nucleic acid purification. Like standard TaqMan gene expression assays, TaqMan miRNA assays exhibit a dynamic range of seven orders of magnitude. Quantification of five miRNAs in seven mouse tissues showed variation from less than 10 to more than 30,000 copies per cell. This method enables fast, accurate and sensitive miRNA expression profiling and can identify and monitor potential biomarkers specific to tissues or diseases. Stem-loop RT-PCR can be used for the quantification of other small RNA molecules such as short interfering RNAs (siRNAs). Furthermore, the concept of stem-loop RT primer design could be applied in small RNA cloning and multiplex assays for better specificity and efficiency.

Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, Lao KQ, Livak KJ, Guegler KJ.Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005 Nov 27;33(20):e179

Plant amiRNA vector

Artificial microRNA (amiRNA) technology is a novel tool in reverse genetic research for discovering or validating gene functions in plants. A convenient cloning strategy has been developed to construct plant amiRNA vectors based on lacO reconstruction and mating-assisted, genetically-integrated cloning (MAGIC). The amiRNA precursor fragment was generated by PCR and inserted into a small donor plasmid through reconstruction of integrated lacO sequence. Blue recombinants were selected on plates containing X-gal and the efficiency of successful clones was 100%. The amiRNA expression cassette was transferred from the donor plasmid to the recipient plasmid p1301-gfp through MAGIC and an amiRNA expression plasmid was created. More than 40 plant amiRNA vectors were generated through this method, one of which was transformed into Arabidopsis thaliana and the target gene was silenced efficiently. The approach will be useful for amiRNA expression vectors construction in plants.

Keywords  amiRNA vectors – Blue–white screening –  lacO reconstruction – MAGIC

Yan H, Zhong X, Jiang S, Zhai C, Ma L. (2010) Improved method for constructing plant amiRNA vectors with blue-white screening and MAGIC. Biotechnol Lett. 2011 Apr 9.http://www.springerlink.com/content/v016845p20453373/fulltext.pdf

comprehensive annotation and discovery of small RNAs from transcriptomic data

Advances in high-throughput next-generation sequencing technology have reshaped the transcriptomic research landscape. However, exploration of these massive data remains a daunting challenge. In this study, we describe a novel database, deepBase, which we have developed to facilitate the comprehensive annotation and discovery of small RNAs from transcriptomic data. The current release of deepBase contains deep sequencing data from 185 small RNA libraries from diverse tissues and cell lines of seven organisms: human, mouse, chicken, Ciona intestinalis, Drosophila melanogaster, Caenhorhabditis elegans and Arabidopsis thaliana. By analyzing ∼14.6 million unique reads that perfectly mapped to more than 284 million genomic loci, we annotated and identified ∼380 000 unique ncRNA-associated small RNAs (nasRNAs), ∼1.5 million unique promoter-associated small RNAs (pasRNAs), ∼4.0 million unique exon-associated small RNAs (easRNAs) and ∼6 million unique repeat-associated small RNAs (rasRNAs). Furthermore, 2038 miRNA and 1889 snoRNA candidates were predicted by miRDeep and snoSeeker. All of the mapped reads can be grouped into about 1.2 million RNA clusters. For the purpose of comparative analysis, deepBase provides an integrative, interactive and versatile display. A convenient search option, related publications and other useful information are also provided for further investigation. deepBase is available at: http://deepbase.sysu.edu.cn/.

Jian-Hua Yang, Peng Shao, Hui Zhou, Yue-Qin Chen, and Liang-Hu Qu. deepBase: a database for deeply annotating and mining deep sequencing data Nucl. Acids Res. (2010) 38(suppl 1): D123-D130 first published online December 4, 2009 oi:10.1093/nar/gkp943