Characterization of global microRNA expression reveals oncogenic potential of miR-145 in metastatic colorectal cancer.

BACKGROUND: MicroRNAs (MiRNAs) are short non-coding RNAs that control protein expression through various mechanisms. Their altered expression has been shown to be associated with various cancers. The aim of this study was to profile miRNA expression in colorectal cancer (CRC) and to analyze the function of specific miRNAs in CRC cells. MirVana miRNA Bioarrays were used to determine the miRNA expression profile in eight CRC cell line models, 45 human CRC samples of different stages, and four matched normal colon tissue samples. SW620 CRC cells were stably transduced with miR-143 or miR-145 expression vectors and analyzed in vitro for cell proliferation, cell differentiation and anchorage-independent growth. Signalling pathways associated with differentially expressed miRNAs were identified using a gene set enrichment analysis. RESULTS: The expression analysis of clinical CRC samples identified 37 miRNAs that were differentially expressed between CRC and normal tissue. Furthermore, several of these miRNAs were associated with CRC tumor progression including loss of miR-133a and gain of miR-224. We identified 11 common miRNAs that were differentially expressed between normal colon and CRC in both the cell line models and clinical samples. In vitro functional studies indicated that miR-143 and miR-145 appear to function in opposing manners to either inhibit or augment cell proliferation in a metastatic CRC model. The pathways targeted by miR-143 and miR-145 showed no significant overlap. Furthermore, gene expression analysis of metastatic versus non-metastatic isogenic cell lines indicated that miR-145 targets involved in cell cycle and neuregulin pathways were significantly down-regulated in the metastatic context. CONCLUSION: MiRNAs showing altered expression at different stages of CRC could be targets for CRC therapies and be further developed as potential diagnostic and prognostic analytes. The identified biological processes and signalling pathways collectively targeted by co-expressed miRNAs in CRC provide a basis for understanding the functional role of miRNAs in cancer.

MicroRNA classifiers for predicting prognosis of squamous cell lung cancer.

Non-small cell lung cancer (NSCLC), which is comprised mainly of adenocarcinoma and squamous cell carcinoma (SCC), is the cause of 80% of all lung cancer deaths in the United States. NSCLC is also associated with a high rate of relapse after clinical treatment and, therefore, requires robust prognostic markers to better manage therapy options. The aim of this study was to identify microRNA (miRNA) expression profiles in SCC of the lung that would better predict prognosis. Total RNA from 61 SCC samples and 10 matched normal lung samples was processed for small RNA species and profiled on MirVana miRNA Bioarrays (version 2, Ambion). We identified 15 miRNAs that were differentially expressed between normal lung and SCC, including members of the miR-17-92 cluster and its paralogues. We also identified miRNAs, including miR-155 and let-7, which had previously been shown to have prognostic value in adenocarcinoma. Based on cross-fold validation analyses, miR-146b alone was found to have the strongest prediction accuracy for stratifying prognostic groups at approximately 78%. The miRNA signatures were superior in predicting overall survival than a previously described 50-gene prognostic signature. Whereas there was no overlap between the mRNAs targeted by the prognostic miRNAs and the 50-gene expression signature, there was a significant overlap in the corresponding biological pathways, including fibroblast growth factor and interleukin-6 signaling. Our data indicate that miRNAs may have greater clinical utility in predicting the prognosis of patients with squamous cell lung carcinomas than mRNA-based signatures.

A 2-gene classifier for predicting response to the farnesyltransferase inhibitor tipifarnib in acute myeloid leukemia.

At present, there is no method available to predict response to farnesyltransferase inhibitors (FTIs). We analyzed gene expression profiles from the bone marrow of patients from a phase 2 study of the FTI tipifarnib in older adults with previously untreated acute myeloid leukemia (AML). The RASGRP1/APTX gene expression ratio was found to predict response to tipifarnib with the greatest accuracy using a "leave one out" cross validation (LOOCV; 96%). RASGRP1 is a guanine nucleotide exchange factor that activates RAS, while APTX (aprataxin) is involved in DNA excision repair. The utility of this classifier for predicting response to tipifarnib was validated in an independent set of 58 samples from relapsed or refractory AML, with a negative predictive value (NPV) and positive predictive value (PPV) of 92% and 28%, respectively (odds ratio of 4.4). The classifier also predicted for improved overall survival (154 vs 56 days; P < .001), which was independent of other covariates, including a previously described prognostic gene expression classifier. Therefore, these data indicate that a 2-gene expression assay may have utility in categorizing a population of patients with AML who are more likely to respond to tipifarnib.