Human papillomaviruses (HPV) are associated with nearly all cervical cancers, 20% to 30% of head and neck cancers (HNC), and other cancers. Because HNCs also arise in HPV-negative patients, this type of cancer provides unique opportunities to define similarities and differences of HPV-positive versus HPVnegative cancers arising in the same tissue.
Using highly sensitive microarray-based procedures, we identified eight microRNAs (miRNAs) showing robust differential expression between 31 laser-capture-microdissected nasopharyngeal carcinomas (NPCs) and 10 normal healthy nasopharyngeal epithelial samples. In particular, miRNA mir-29c was expressed at one-fifth the levels in tumors as in normal epithelium. In NPC tumors, the lower mir-29c levels correlated with higher levels of multiple mRNAs whose 3 UTRs can bind mir-29c at target sequences conserved across many vertebrates. In cultured cells, introduction of mir-29c down-regulated these genes at the level of mRNA and inhibited expression of luciferase encoded by vectors having the 3 UTRs of these genes. Moreover, for each of several genes tested, mutating the mir-29c target sites in the 3 UTR abrogated mir-29c-induced inhibition of luciferase expression. Most of the mir-29c-targeted genes identified encode extracellular matrix proteins, including multiple collagens and laminin ␥1, that are associated with tumor cell invasiveness and metastatic potential, prominent characteristics of NPC. Thus, we identify eight miRNAs differentially expressed in NPC and demonstrate the involvement of one in regulating genes involved in metastasis.microarray ͉ collagen ͉ metastasis ͉ miRNA M icroRNAs (miRNAs) are short (Ϸ22 nucleotides) noncoding RNAs involved in posttranscriptional silencing of target genes. In animals, miRNAs control expression of target genes by inhibiting translation, by degrading target mRNAs, or both, through binding to their 3Ј UTRs with varying degrees of sequence complementarity (1). miRNAs have been found to regulate genes involved in diverse biological functions, including development, differentiation, proliferation, and stress response (2). Recently, a growing number of miRNAs have been implicated in cancers, including mir-15 and mir-16 in B cell chronic lymphocytic leukemias (3, 4); mir-143 and mir-145 in colorectal cancer (5); mir- 17-5p, mir-21, mir-125b, mir-145, and mir-155 in breast cancer (6, 7); mir-19, mir-146, mir-181b, mir-221, mir-222, and mir-346 in thyroid cancer (8-10); and mir-21 in glioblastoma (11). A significant number of miRNAs also have been mapped to cancer-associated genomic regions (12). Expression of miRNA let-7 has been correlated with prognosis in lung cancer (13) and found to regulate Ras in the same tumor (14). Very recently, mir-10b has been shown to contribute to metastasis in breast cancer (15). Although many miRNAs have been implicated in regulating cancers, very few of their target genes, and hence their downstream mode of action, have been identified.We developed a sensitive microarray-based assay to profile miRNA expression and used it to analyze human miRNAs in laser-microdissected tumor and normal cells from biopsies of a highly invasive cancer, nasopharyngeal carcinoma (NPC), and site-matched normal tissues. Eight miRNAs were differentially expressed. One of them, mir-29c, down-regulated in NPC, was shown to target multiple mRNAs encoding extracellular ma...
A prespecified set of genes may be enriched, to varying degrees, for genes that have altered expression levels relative to two or more states of a cell. Knowing the enrichment of gene sets defined by functional categories, such as gene ontology (GO) annotations, is valuable for analyzing the biological signals in microarray expression data. A common approach to measuring enrichment is by crossclassifying genes according to membership in a functional category and membership on a selected list of significantly altered genes. A small Fisher's exact test p-value, for example, in this 2 × 2 table is indicative of enrichment. Other category analysis methods retain the quantitative gene-level scores and measure significance by referring a category-level statistic to a permutation distribution associated with the original differential expression problem. We describe a class of random-set scoring methods that measure distinct components of the enrichment signal. The class includes Fisher's test based on selected genes and also tests that average gene-level evidence across the category. Averaging and selection methods are compared empirically using Affymetrix data on expression in nasopharyngeal cancer tissue, and theoretically using a location model of differential expression. We find that each method has a domain of superiority in the state space of enrichment problems, and that both methods have benefits in practice. Our analysis also addresses two problems related to multiple-category inference, namely, that equally enriched categories are not detected with equal probability if they are of different sizes,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.