Long non-coding RNA expression profiling in the NCI60 cancer cell line panel using high-throughput RT-qPCR.
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ABSTRACT:
SUBMITTER: Mestdagh P
PROVIDER: S-EPMC4932877 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1708 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response.
2016-06-29 | GSE80332 | GEO
Project description:Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1708 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response. lncRNA expression profiling of 60 cancer cell lines
2016-06-29 | E-GEOD-80332 | biostudies-arrayexpress
Project description:MicroRNAs (miRNAs) are an emerging class of small non-coding RNAs implicated in a wide variety of cellular processes. Research in this field is accelerating, and the growing number of miRNAs emphasizes the need for high-throughput and sensitive detection methods. Here we present the successful evaluation of the Megaplex reverse transcription format of the stem-loop primer-based real-time quantitative polymerase chain reaction (RT-qPCR) approach to quantify miRNA expression. The Megaplex reaction provides simultaneous reverse transcription of 450 mature miRNAs, ensuring high-throughput detection. Further, the introduction of a complementary DNA pre-amplification step significantly reduces the amount of input RNA needed, even down to single-cell level. To evaluate possible pre-amplification bias, we compared the expression of 384 miRNAs in three different cancer cell lines with Megaplex RT, with or without an additional pre-amplification step. The normalized Cq values of all three sample pairs showed a good correlation with maintenance of differential miRNA expression between the cell lines. Moreover, pre-amplification using 10 ng of input RNA enabled the detection of miRNAs that were undetectable when using Megaplex alone with 400 ng of input RNA. The high specificity of RT-qPCR together with a superior sensitivity makes this approach the method of choice for high-throughput miRNA expression profiling.
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Project description:A long-sought milestone in microfluidics research has been the development of integrated technology for scalable analysis of transcription in single cells. Here we present a fully integrated microfluidic device capable of performing high-precision RT-qPCR measurements of gene expression from hundreds of single cells per run. Our device executes all steps of single-cell processing, including cell capture, cell lysis, reverse transcription, and quantitative PCR. In addition to higher throughput and reduced cost, we show that nanoliter volume processing reduced measurement noise, increased sensitivity, and provided single nucleotide specificity. We apply this technology to 3,300 single-cell measurements of (i) miRNA expression in K562 cells, (ii) coregulation of a miRNA and one of its target transcripts during differentiation in embryonic stem cells, and (iii) single nucleotide variant detection in primary lobular breast cancer cells. The core functionality established here provides the foundation from which a variety of on-chip single-cell transcription analyses will be developed.
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Project description:Only 3% of the transcribed human genome is translated into protein, and small non-coding RNAs from these untranslated regions have demonstrated critical roles in transcriptional and translational regulation of proteins. Here, we provide a resource that will facilitate cell line selection for gene expression studies involving sncRNAs in cancer research. As the most accessible and tractable models of tumours, cancer cell lines are widely used to study cancer development and progression. The NCI-60 panel of 59 cancer cell lines was curated to provide common models for drug screening in 9 tissue types; however, its prominence has extended to use in gene regulation, xenograft models, and beyond. Here, we present the complete small non-coding RNA (sncRNA) transcriptomes of these 59 cancer cell lines. Additionally, we examine the abundance and unique sequences of annotated microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs), and reveal novel unannotated microRNA sequences.
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Project description:MotivationRecent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.ResultsWe modelled the 50% growth inhibition bioassay end-point (GI50) of 17,142 compounds screened against 59 cancer cell lines from the NCI60 panel (941,831 data-points, matrix 93.08% complete) by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data (Tukey's Honestly Significant Difference, P <0.05). We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines.Contactterez@pasteur.fr; ab454@ac.cam.ukSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Tumor stem cells or cancer initiating cells (CICs) are single tumor cells that can regenerate a tumor or a metastasis. The identification and isolation of CICs remain challenging, and a variety of putative CIC markers have been described. We hypothesized that cell lines of the NCI60 panel contain CICs and express putative CIC markers. We investigated expression of putative CIC surface markers (CD15, CD24, CD44, CD133, CD166, CD326, PgP) and the activity of aldehyde dehydrogenase in the NCI60 panel singly and in combination by six-color fluorescence-activated cell sorting analysis. All investigated markers were expressed in cell lines of the NCI60 panel. Expression levels of individual markers varied widely across the 60 cell lines, and neither single marker expression nor simple combinations nor co-expression patterns correlated with the colony-formation capacity of cell lines. Rather, marker expression patterns correlated with tumor types in multidimensional analysis. Whereas some expression patterns correlated with tumor entities such as basal breast cancer, other expression patterns occurred across different tumor types and largely related to expression of a more mesenchymal phenotype in individual breast, lung, renal, and melanoma cell lines. Our data for the first time demonstrate that tumor cell lines display CIC markers in a complex pattern that relates to the tumor type. The complexity and tumor type specificity of marker display creates challenges for the application of cell sorting and other approaches to isolation of putative tumor stem cell populations and suggests that therapeutic targeting strategies will need to take this into account.
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Project description:BackgroundMedulloblastoma (MB) is one of the most common malignant cancers in children. MB is primarily classified into four subgroups based on molecular and clinical characteristics as (1) WNT (2) Sonic-hedgehog (SHH) (3) Group 3 (4) Group 4. Molecular characteristics used for MB classification are based on genomic and mRNAs profiles. MB subgroups share genomic and mRNA profiles and require multiple molecular markers for differentiation from each other. Long non-coding RNAs (lncRNAs) are more than 200 nucleotide long RNAs and primarily involve in gene regulation at epigenetic and post-transcriptional levels. LncRNAs have been recognized as diagnostic and prognostic markers in several cancers. However, the lncRNA expression profile of MB is unknown.MethodsWe used the publicly available gene expression datasets for the profiling of lncRNA expression across MB subgroups. Functional analysis of differentially expressed lncRNAs was accomplished by Ingenuity pathway analysis (IPA).ResultsIn the current study, we have identified and validated the lncRNA expression profile across pediatric MB subgroups and associated molecular pathways. We have also identified the prognostic significance of lncRNAs and unique lncRNAs associated with each MB subgroup.ConclusionsIdentified lncRNAs can be used as single biomarkers for molecular identification of MB subgroups that warrant further investigation and functional validation.
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Project description:Circulating cell-free microRNAs are promising candidates for minimally invasive clinical biomarkers for the diagnosis, prognosis and monitoring of many human diseases. Despite substantial efforts invested in the field, the research so far has failed to deliver expected results. One of the contributing factors is general lack of agreement between various studies, partly due to the considerable technical challenges accompanying the workflow. Pre-analytical variables including sample collection, RNA isolation, and quantification are sources of bias that may hamper biological interpretation of the results. Here, we present a Two-tailed RT-qPCR panel for quality control, monitoring of technical performance, and optimization of microRNA profiling experiments from biofluid samples. The Two-tailed QC (quality control) panel is based on two sets of synthetic spike-in molecules and three endogenous microRNAs that are quantified with the highly specific Two-tailed RT-qPCR technology. The QC panel is a cost-effective way to assess quality of isolated microRNA, degree of inhibition, and erythrocyte contamination to ensure technical soundness of the obtained results. We provide assay sequences, detailed experimental protocol and guide to data interpretation. The application of the QC panel is demonstrated on the optimization of RNA isolation from biofluids with the miRNeasy Serum/Plasma Advanced Kit (Qiagen).
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Project description:Oral epithelial dysplasias are believed to progress through a series of histopathological stages; from mild to severe dysplasia, to carcinoma in situ, and finally to invasive OSCC. Underlying this change in histopathological grade are gross chromosome alterations and changes in gene expression of both protein-coding genes and non-coding RNAs. Recent papers have described associations of aberrant expression of microRNAs, one class of non-coding RNAs, with oral cancer. However, expression profiling of long non-coding RNAs (lncRNAs) has not been reported. Long non-coding RNAs are a novel class of mRNA-like transcripts with no protein coding capacity, but with a variety of functions including roles in epigenetics and gene regulation. In recent reports, the aberrant expression of lncRNAs has been associated with human cancers, suggesting a critical role in tumorigenesis. Here, we present the first long non-coding RNA expression map for the human oral mucosa. We describe the expression of 325 long non-coding RNAs, suggesting lncRNA expression contributes significantly to the oral transcriptome. Intriguingly, 60% of the detected lncRNAs show aberrant expression in oral premalignant lesions. A number of these lncRNAs have been previously associated with other human cancers. A total of six normal oral samples and ten oral premalignant lesions were used to construct SAGE libraries which were then queried for long non-coding RNA expression profiles. The six normal oral samples were previously deposited as GSE8127.
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