Expression profile of microRNAs in gastrointestinal stromal tumors revealed by high throughput quantitative RT-PCR microarray.

AIM: To investigate the microRNA (miRNA) expression profile in gastrointestinal stromal tumor (GIST) tissues that could serve as a novel diagnostic biomarker for GIST detection. METHODS: We performed a quantitative real-time quantitative reverse transcriptase polymerase chain reaction assay to analyze the expression of 1888 miRNAs in a sample set that included 54 GIST tissue samples. RESULTS: We found that dysregulation of several miRNAs may be related to the malignant potential of GISTs. Six of these miRNAs, hsa-let-7c, miR-218, miR-488#, miR-4683, miR-34c-5p and miR-4773, were selected as the final list of biomarkers to separate the malignant GISTs (M group) from the benign GISTs (B group). In addition, MiR-29b-2#, hsa-let-7c, miR-891b, miR-218, miR-204, miR-204-3p, miR-628-5p, miR-744, miR-29c#, miR-625 and miR-196a were used to distinguish between the borderline (BO group) and M groups. There were 11 common miRNAs selected to separate the benign and borderline (BB) group from the M group, including hsa-let-7c, miR-218, miR-628-5p, miR-204-3p, miR-204, miR-891b, miR-488#, miR-145, miR-891a, miR-34c-5p and miR-196a. CONCLUSION: The identified miRNAs appear to be novel biomarkers to distinguish malignant from benign GISTs, which may be helpful to understand the mechanisms of GIST oncogenesis and progression, and to further elucidate the characteristics of GIST subtypes.

Liposarcoma miRNA signatures identified from genome-wide miRNA expression profiling.

AIMS: To identify the miRNA expression profile of liposarcoma (LPS) that could facilitate detection of LPS, and provide the basis for further investigation of molecular-targeted therapeutic drugs. MATERIALS & METHODS: A real-time quantitative PCR assay was performed to analyze the expression of 1888 miRNAs from 25 LPS tumor tissue samples, 16 samples of adipose tissue adjacent to the tumors and 18 normal adipose tissue samples from patients with LPS. RESULTS: Ten dysregulated miRNAs were identified that effectively distinguished LPS tissue from adipose tissue and benign lipoma tissue, and LPS tumor tissues from normal adipose tissues in LPS patients. Furthermore, the expression profiles of miRNAs could also classify the subtype of LPS. CONCLUSION: The identified miRNAs appear to be novel biomarkers for the detection of LPS, and may contribute to an understanding of the mechanisms of LPS tumorigenesis and its development, and further elucidate the characteristics of LPS subtypes.

A ten-microRNA signature identified from a genome-wide microRNA expression profiling in human epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is the most common gynecologic malignancy. To identify the micro-ribonucleic acids (miRNAs) expression profile in EOC tissues that may serve as a novel diagnostic biomarker for EOC detection, the expression of 1722 miRNAs from 15 normal ovarian tissue samples and 48 ovarian cancer samples was profiled by using a quantitative real-time polymerase chain reaction (qRT-PCR) assay. A ten-microRNA signature (hsa-miR-1271-5p, hsa-miR-574-3p, hsa-miR-182-5p, hsa-miR-183-5p, hsa-miR-96-5p, hsa-miR-15b-5p, hsa-miR-182-3p, hsa-miR-141-5p, hsa-miR-130b-5p, and hsa-miR-135b-3p) was identified to be able to distinguish human ovarian cancer tissues from normal tissues with 97% sensitivity and 92% specificity. Two miRNA clusters of miR183-96-183 (miR-96-5p, and miR-182, miR183) and miR200 (miR-141-5p, miR200a, b, c and miR429) are significantly up-regulated in ovarian cancer tissue samples compared to those of normal tissue samples, suggesting theses miRNAs may be involved in ovarian cancer development.

MicroRNA programs in normal and aberrant stem and progenitor cells.

Emerging evidence suggests that microRNAs (miRNAs), an abundant class of ∼22-nucleotide small regulatory RNAs, play key roles in controlling the post-transcriptional genetic programs in stem and progenitor cells. Here we systematically examined miRNA expression profiles in various adult tissue-specific stem cells and their differentiated counterparts. These analyses revealed miRNA programs that are common or unique to blood, muscle, and neural stem cell populations and miRNA signatures that mark the transitions from self-renewing and quiescent stem cells to proliferative and differentiating progenitor cells. Moreover, we identified a stem/progenitor transition miRNA (SPT-miRNA) signature that predicts the effects of genetic perturbations, such as loss of PTEN and the Rb family, AML1-ETO9a expression, and MLL-AF10 transformation, on self-renewal and proliferation potentials of mutant stem/progenitor cells. We showed that some of the SPT-miRNAs control the self-renewal of embryonic stem cells and the reconstitution potential of hematopoietic stem cells (HSCs). Finally, we demonstrated that SPT-miRNAs coordinately regulate genes that are known to play roles in controlling HSC self-renewal, such as Hoxb6 and Hoxa4. Together, these analyses reveal the miRNA programs that may control key processes in normal and aberrant stem and progenitor cells, setting the foundations for dissecting post-transcriptional regulatory networks in stem cells.

Quantitation of microRNAs by real-time RT-qPCR.

MicroRNAs (miRNAs) are ∼22 nucleotide regulatory RNA molecules that play important roles in controlling developmental and physiological processes in animals and plants. Measuring the level of miRNA expression is a critical step in methods that study the regulation of biological functions and that use miRNA profiles as diagnostic markers for cancer and other diseases. Even though the quantitation of these small miRNA molecules by RT-qPCR is challenging because of their short length and sequence similarity, a number of quantitative RT-qPCR-based miRNA quantitation methods have been introduced since 2004. The most commonly used methods are stem-loop reverse transcription (RT)-based TaqMan(®) MicroRNA assays and arrays. The high sensitivity and specificity, large dynamic range, and simple work flow of TaqMan(®) MicroRNA assays and arrays have made TaqMan analysis the method of choice for miRNA expression profiling and follow-up validation. Other methods such as poly (A) tailing-based and direct RT-based SYBR miRNA assays are also discussed in this chapter.

Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations.

Due to growing throughput and shrinking cost, massively parallel sequencing is rapidly becoming an attractive alternative to microarrays for the genome-wide study of gene expression and copy number alterations in primary tumors. The sequencing of transcripts (RNA-Seq) should offer several advantages over microarray-based methods, including the ability to detect somatic mutations and accurately measure allele-specific expression. To investigate these advantages we have applied a novel, strand-specific RNA-Seq method to tumors and matched normal tissue from three patients with oral squamous cell carcinomas. Additionally, to better understand the genomic determinants of the gene expression changes observed, we have sequenced the tumor and normal genomes of one of these patients. We demonstrate here that our RNA-Seq method accurately measures allelic imbalance and that measurement on the genome-wide scale yields novel insights into cancer etiology. As expected, the set of genes differentially expressed in the tumors is enriched for cell adhesion and differentiation functions, but, unexpectedly, the set of allelically imbalanced genes is also enriched for these same cancer-related functions. By comparing the transcriptomic perturbations observed in one patient to his underlying normal and tumor genomes, we find that allelic imbalance in the tumor is associated with copy number mutations and that copy number mutations are, in turn, strongly associated with changes in transcript abundance. These results support a model in which allele-specific deletions and duplications drive allele-specific changes in gene expression in the developing tumor.

E. coli selection of human genes encoding secreted and membrane proteins based on cDNA fusions to a leaderless beta-lactamase reporter.

Although several signal peptide-trapping methods have been devised and used to detect signal sequences, none have relied on using E.coli to identify eukaryotic proteins with signal peptides. Here, we describe a system for selecting human secreted and membrane proteins in E. coli followed by the direct validation of secretion in human cells. The method is based on cDNA fusions to a leaderless beta-lactamase reporter gene to isolate clones encoding signal peptides of human genes. We found that beta-lactamase fusion proteins carrying a eukaryotic signal peptide at its N-terminus were able to direct their export into the periplasm in E. coli to confer survival upon challenge with carbenicillin. When libraries constructed from 5' end-enriched cDNAs fused to beta-lactamase were screened in E.coli, approximately 0.5%-1% of the cDNAs are selected, and over half of the surviving clones were found to encode for secreted fusion proteins when tested in human cells. These clones were sequenced and shown to represent human genes encoding signal peptides of secreted and membrane proteins. We conclude that this is an efficient and effective strategy to easily enrich cDNA libraries for the identification of novel genes likely to encode secreted enzymes, growth factors, and receptors.