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      Correlation of Global MicroRNA Expression With Basal Cell Carcinoma Subtype

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          Basal cell carcinomas (BCCs) are the most common cancers in the United States. The histologic appearance distinguishes several subtypes, each of which can have a different biologic behavior. In this study, global miRNA expression was quantified by high-throughput sequencing in nodular BCCs, a subtype that is slow growing, and infiltrative BCCs, aggressive tumors that extend through the dermis and invade structures such as cutaneous nerves. Principal components analysis correctly classified seven of eight infiltrative tumors on the basis of miRNA expression. The remaining tumor, on pathology review, contained a mixture of nodular and infiltrative elements. Nodular tumors did not cluster tightly, likely reflecting broader histopathologic diversity in this class, but trended toward forming a group separate from infiltrative BCCs. Quantitative polymerase chain reaction assays were developed for six of the miRNAs that showed significant differences between the BCC subtypes, and five of these six were validated in a replication set of four infiltrative and three nodular tumors. The expression level of miR-183, a miRNA that inhibits invasion and metastasis in several types of malignancies, was consistently lower in infiltrative than nodular tumors and could be one element underlying the difference in invasiveness. These results represent the first miRNA profiling study in BCCs and demonstrate that miRNA gene expression may be involved in tumor pathogenesis and particularly in determining the aggressiveness of these malignancies.

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          Most cited references 53

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site ( Contact:
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              Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets.

              We predict regulatory targets of vertebrate microRNAs (miRNAs) by identifying mRNAs with conserved complementarity to the seed (nucleotides 2-7) of the miRNA. An overrepresentation of conserved adenosines flanking the seed complementary sites in mRNAs indicates that primary sequence determinants can supplement base pairing to specify miRNA target recognition. In a four-genome analysis of 3' UTRs, approximately 13,000 regulatory relationships were detected above the estimate of false-positive predictions, thereby implicating as miRNA targets more than 5300 human genes, which represented 30% of our gene set. Targeting was also detected in open reading frames. In sum, well over one third of human genes appear to be conserved miRNA targets.

                Author and article information

                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                1 February 2012
                February 2012
                : 2
                : 2
                : 279-286
                [* ]Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520
                []Department of Genetics, Stanford University School of Medicine, Stanford, CA
                []Howard Hughes Medical Institute and Program in Epithelial Biology, Stanford University, Stanford, CA
                [§ ]Department of Dermatology, Yale University School of Medicine, New Haven, CT
                [** ]Yale Comprehensive Cancer Center, New Haven, CT
                [†† ]Department of Genetics, Yale University School of Medicine, New Haven, CT 06520-8005
                [‡‡ ]Biostatics Resources, Keck Laboratory, Yale University, New Haven, CT 06520
                [§§ ]Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520
                Author notes

                Supporting information is available online at

                Raw and processed miRNA sequencing expression data from this article have been deposited with the Gene Expression Omnibus (GEO) study under accession number GSE33665.


                Joint senior authors.

                [2 ]Corresponding authors: Department of Genetics, Yale University School of Medicine, 333 Cedar St., New Haven, CT 06520-8005; Department of Genetics, Stanford University School of Medicine, 300 Pasteur Dr., Stanford, CA 94305.  E-mail: allen.bale@ ; mpsnyder@
                Copyright © 2012 Heffelfinger et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                expression profiling, histopathology, mir-150, skin cancer, mir-183


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