ABSTRACT
Recent progress has been made in the identification of protein-coding genes and miRNAs that are expressed in and alter the behavior of colonic epithelia. However, the role of long non-coding RNAs (lncRNAs) in colonic homeostasis is just beginning to be explored. By gene expression profiling of post-mitotic, differentiated tops and proliferative, progenitor-compartment bottoms of microdissected adult mouse colonic crypts, we identified several lncRNAs more highly expressed in crypt bottoms. One identified lncRNA, designated non-coding Nras functional RNA (ncNRFR), resides within the Nras locus but appears to be independent of the Nras coding transcript. Stable overexpression of ncNRFR in non-transformed, conditionally immortalized mouse colonocytes results in malignant transformation, as determined by growth in soft agar and formation of highly invasive tumors in nude mice. Moreover, ncNRFR appears to inhibit the function of the tumor suppressor let-7. These results suggest precise regulation of ncNRFR is necessary for proper cell growth in the colonic crypt, and its misregulation results in neoplastic transformation.
Subject(s)
Cell Transformation, Neoplastic , Colon/pathology , Colonic Neoplasms/genetics , Epithelial Cells/pathology , Gene Expression Regulation, Neoplastic , RNA, Long Noncoding/genetics , Animals , Colon/metabolism , Colonic Neoplasms/metabolism , Colonic Neoplasms/pathology , Epithelial Cells/metabolism , Gene Expression Profiling , Mice , Mice, Nude , MicroRNAs/genetics , MicroRNAs/metabolism , RNA, Long Noncoding/metabolismABSTRACT
Matrix metalloproteinase-7 (MMP-7) is a small secreted proteolytic enzyme with broad substrate specificity against ECM and non-ECM components. Known to be vital for tumor invasion and metastasis, accumulating evidence also implicates MMP-7 in cancer development. Using data from the Shanghai Breast Cancer Study, we conducted a two-stage study to evaluate the association of MMP-7 single nucleotide polymorphisms (SNPs) with breast cancer risk. Additionally, associated SNPs were characterized by laboratory assays. In stage 1, 11 SNPs were genotyped among 1,079 incident cases and 1,082 community controls using an Affymetrix Genotyping System. Promising SNPs were selected for stage 2 evaluation and genotyped by TaqMan allelic discrimination assays in an independent set of 1,911 cases and 1,811 controls. Three SNPs were selected for stage 2 validation (rs880197, rs10895304, and rs12184413); one had highly consistent results between the two stages of the study. In combined analysis, homozygosity for the variant T allele for rs12184413 was associated with an odds ratio (OR) of 0.7 [95% confidence interval (95% CI), 0.6-0.9] compared with the common C allele. This effect was slightly more pronounced in postmenopausal women (OR, 0.6; 95% CI, 0.4-0.8) than in premenopausal women (OR, 0.8; 95% CI, 0.6-1.1). This SNP is located 3' of the MMP-7 gene, in an area enriched with CTCF binding sites. In silico analysis suggested a regulatory role for this region, and our in vitro assays showed an allelic difference in nuclear protein binding capacity. Results from our study suggest that common MMP-7 genetic polymorphisms may contribute to breast cancer susceptibility.
Subject(s)
Breast Neoplasms/genetics , Genetic Predisposition to Disease , Matrix Metalloproteinase 7/genetics , Polymorphism, Single Nucleotide , Adult , Base Sequence , Case-Control Studies , DNA Primers , Electrophoretic Mobility Shift Assay , Female , Humans , Middle AgedABSTRACT
High-throughput experiments in biology often produce sets of genes of potential interests. Some of those gene sets might be of considerable size. Therefore, computer-assisted analysis is necessary for the biological interpretation of the gene sets, and for creating working hypotheses, which can be tested experimentally. One obvious way to analyze gene set data is to associate the genes with a particular biological feature, for example, a given pathway. Statistical analysis could be used to evaluate if a gene set is truly associated with a feature. Over the past few years many tools that perform such analysis have been created. In this chapter, using WebGestalt as an example, it will be explained in detail how to associate gene sets with functional annotations, pathways, publication records, and protein domains.
Subject(s)
Databases, Genetic , Genetic Techniques/statistics & numerical data , Software , Computational Biology , Data Interpretation, Statistical , Gene Expression Profiling/statistics & numerical data , Genomics/statistics & numerical data , Oligonucleotide Array Sequence Analysis/statistics & numerical dataABSTRACT
Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for co-regulation is detected through the use of quantitative trait locus mapping.
