| Literature DB >> 24373327 |
Carlos Stahlhut1, Frank J Slack1.
Abstract
MicroRNAs (miRNAs) have emerged as key genetic regulators of a wide variety of biological processes, including growth, proliferation, and survival. Recent advances have led to the recognition that miRNAs can act as potent oncogenes and tumor suppressors, playing crucial roles in the initiation, maintenance, and progression of the oncogenic state in a variety of cancers. Determining how miRNA expression and function is altered in cancer is an important goal, and a necessary prerequisite to the development and adoption of miRNA-based therapeutics in the clinic. Highly promising clinical applications of miRNAs are the use of miRNA signatures as biomarkers for cancer (for example, for early detection or diagnosis), and therapeutic supplementation or inhibition of specific miRNAs to alter the cancer phenotype. In this review, we discuss the main methods used for miRNA profiling, and examine key miRNAs that are commonly altered in a variety of tumors. Current studies underscore the functional versatility and potency of miRNAs in various aspects of the cancer phenotype, pointing to their potential clinical applications. Consequently, we discuss the application of miRNAs as biomarkers, clinical agents, and therapeutic targets, highlighting both the enormous potential and major challenges in this field.Entities:
Year: 2013 PMID: 24373327 PMCID: PMC3978829 DOI: 10.1186/gm516
Source DB: PubMed Journal: Genome Med ISSN: 1756-994X Impact factor: 11.117
Major platforms for microRNA profiling
| qPCR | <6 hours | 10-500 ng | <$300 | High sensitivity, large dynamic range (~6 orders of magnitude), reproducibility, accessibility, compatible with several sources of biological material | Low throughput, unable to detect novel or modified miRNAs, variability across platforms |
| Microfluidic qPCR platforms | <6 hours | 10-500 ng | <$300 | High throughput, high sensitivity, large dynamic range (~6 orders of magnitude), reproducibility, compatible with several sources of biological material | Unable to detect novel or modified miRNAs, variability across platforms |
| Microarray | 48 hours | 100 ng-1 μg | ~$300 | High throughput, well optimized, well established analysis methods | Generally cannot distinguish mature from pre-miRNAs, might require a non-specific amplification step |
| Next generation sequencing | 2 weeks | 500 ng-5 μg | >$1000 | High throughput, useful for miRNA discovery and modified miRNA detection, high sensitivity, large dynamic range (~5 orders of magnitude) | Large investment and bioinformatics expertise required, slow turnover, may suffer from non-linearity in the amplification step |
miRNA, microRNA; qPCR, quantitative polymerase chain reaction.
MicroRNAs commonly affected in cancer and their targets
| miR-17 ~ 92 cluster [ | OG/TS | Lung, breast, pancreas, colon, BCL, retinoblastoma, glioblastoma | HIF-1α (hypoxia response) |
| PTEN, E2F1-3, TNF-α, RAB14 (cell proliferation) | |||
| BIM, TGFBR2 (cell survival) | |||
| TSP1, CTGF (tumor angiogenesis) | |||
| miR-21 [ | OG | Lung, breast, lymphoma, glioblastoma | PTEN, SPRY1, SPRY2, (cell proliferation) |
| PDCD4, APAF1 (cell survival) | |||
| TPM-1, TPM-3, RECK (metastasis) | |||
| miR-155 [ | OG | Lung, lymphoma, breast | FOXO3A, SHIP1, SOCS1 (cell survival) |
| RhoA (metastasis) | |||
| miR-221 or miR-222 [ | OG | Lung, glioblastoma | KIT, p27, PUMA (cell survival) |
| PTEN (cell proliferation) | |||
| TS | Lung, lymphoma, gastric, prostate, breast, ovarian | KRAS, NRAS, CDC25A, c-MYC (cell proliferation) | |
| HMGA2 (metastasis) | |||
| miR-34 [ | TS | Lung, lymphoma, pancreas, colon, neuroblastoma, glioblastoma | CDC25A, CDK4, CDK6, c-MYC (cell proliferation) |
| MET (metastasis) | |||
| BCL2 (cell survival) | |||
| miR-15/16 [ | TS | CLL, multiple myeloma, prostate, pancreas | BCL2 (cell survival) |
| CDC2, JUN, FGF-2, CCND-1 (cell proliferation) | |||
| miR-200 [ | TS | Breast, renal, gastric, bladder | ZEB1, ZEB2 (cell differentiation and metastasis) |
| miR-181 [ | TS | Glioma, lymphoma | TCL1 (cell survival) |
| miR-29 [ | TS | CLL, hepatocellular carcinoma, breast | MCL1, TCL1 (cell survival) |
| DNMT1 (gene expression) |
BCL, B-cell lymphoma; CLL, chronic lymphocytic leukemia; OG, oncogene; TS, tumor suppressor.
Figure 1is a potent tumor suppressor microRNA. The let-7 microRNA (miRNA) can affect several key cellular pathways (such as those involved in the cell cycle, cell growth, cell proliferation, migration or invasion) by targeting key effector genes. In cancer, the function of let-7 is often lost or impaired through chromosomal deletions, mutations in the target sequence of regulated mRNAs, mutations in the seed site of the miRNA, or through post-transcriptional regulation by proteins such as LIN-28, whose expression is driven by potent oncogenes such as MYC and nuclear factor (NF)-κB.
Figure 2miR-34 can affect tumor initiation and progression by regulating a variety of key cellular functions. mir-34 is a transcriptional target of the tumor suppressor protein p53. This microRNA can regulate key genes involved in cell proliferation, survival, and invasion. In cancer, chromosomal aberrations can disrupt the mir-34 locus, and its transcription can also be inhibited by methylation changes and by inhibition or loss of p53.