Influence of RNA Labeling on Expression Profiling of MicroRNAs
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ABSTRACT:
PROVIDER: PRJNA144079 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Several technical parameters are now being optimized for microRNA expression profiling experiments, but the finite amount of total RNA available continues to bottleneck clinical investigations. We used microarrays to determine the influence of the labeling reagent on total RNA requirements and data quality.
2012-05-22 | GSE30045 | GEO
Project description:Several technical parameters are now being optimized for microRNA expression profiling experiments, but the finite amount of total RNA available continues to bottleneck clinical investigations. We used microarrays to determine the influence of the labeling reagent on total RNA requirements and data quality. Human brain/lung samples were each labeled in duplicate, at 1.0ug, 0.5ug, 0.2ug, and 0.1ug of total RNA, using two microRNA labeling kits (Genisphere Biotin and Biotin-HSR) that utilize the same labeling procedure but differ in the composition of the labeling reagent used to label the microRNA molecules in the samples. A total of 32 arrays were used for this experiment. Additionally, synthetic microRNA spike-in experiments were also performed to establish the signal dynamic range of the microarray using the Biotin-HSR labeling kit. Two-fold serial dilution samples were prepared that contained from 5000 to 4.88 attomoles of miRNA. A total of 12 arrays were used for this experiment, including a negative control sample.
2012-05-21 | E-GEOD-30045 | biostudies-arrayexpress
Project description:Although a number of technical parameters are now being examined to optimize microRNA profiling experiments, it is unknown whether reagent or component changes to the labeling step affect starting RNA requirements or microarray performance. Human brain/lung samples were each labeled in duplicate, at 1.0, 0.5, 0.2, and 0.1 ?g of total RNA, by means of two kits that use the same labeling procedure but differ in the reagent composition used to label microRNAs. Statistical measures of reliability and validity were used to evaluate microarray data. Cross-platform confirmation was accomplished using TaqMan microRNA assays. Synthetic microRNA spike-in experiments were also performed to establish the microarray signal dynamic range using the ligation-modified kit. Technical replicate correlations of signal intensity values were high using both kits, but improved with the ligation-modified assay. The drop in detection call sensitivity and miRNA gene list correlations, when using reduced amounts of standard-labeled RNA, was considerably improved with the ligation-modified kit. Microarray signal dynamic range was found to be linear across three orders of magnitude from 4.88 to 5000 attomoles. Thus, optimization of the microRNA labeling reagent can result in at least a 10-fold decrease in microarray total RNA requirements with little compromise to data quality. Clinical investigations bottlenecked by the amount of starting material may use a ligation mix modification strategy to reduce total RNA requirements.
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Project description:microRNA dysregulation is a common feature of cancer cells, but the complex roles of microRNAs in cancer are not fully elucidated. Here we used functional genomics to identify oncogenic microRNAs in non-small cell lung cancer and to evaluate their impact on response to EGFR targeting therapy. Our data demonstrate that microRNAs with an AAGUGC-motif in their seed-sequence increase both cancer cell proliferation and sensitivity to EGFR inhibitors. Global transcriptomics, proteomics and target prediction resulted in the identification of several tumor suppressors involved in the G1/S transition as targets of AAGUGC-microRNAs. The clinical implications of our findings were evaluated by analysis of public domain data supporting the link between this microRNA seed-family, their tumor suppressor targets and cancer cell proliferation. In conclusion we propose that AAGUGC-microRNAs are an integral part of an oncogenic signaling network, and that these findings have potential therapeutic implications, especially in selecting patients for EGFR-targeting therapy.
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Project description:microRNA dysregulation is a common feature of cancer cells, but the complex roles of microRNAs in cancer are not fully elucidated. Here we used functional genomics to identify oncogenic microRNAs in non-small cell lung cancer and to evaluate their impact on response to EGFR targeting therapy. Our data demonstrate that microRNAs with an AAGUGC-motif in their seed-sequence increase both cancer cell proliferation and sensitivity to EGFR inhibitors. Global transcriptomics, proteomics and target prediction resulted in the identification of several tumor suppressors involved in the G1/S transition as targets of AAGUGC-microRNAs. The clinical implications of our findings were evaluated by analysis of public domain data supporting the link between this microRNA seed-family, their tumor suppressor targets and cancer cell proliferation. In conclusion we propose that AAGUGC-microRNAs are an integral part of an oncogenic signaling network, and that these findings have potential therapeutic implications, especially in selecting patients for EGFR-targeting therapy.
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