ABSTRACT
We studied miRNA profiles in 4419 human samples (3312 neoplastic, 1107 nonmalignant), corresponding to 50 normal tissues and 51 cancer types. The complexity of our database enabled us to perform a detailed analysis of microRNA (miRNA) activities. We inferred genetic networks from miRNA expression in normal tissues and cancer. We also built, for the first time, specialized miRNA networks for solid tumors and leukemias. Nonmalignant tissues and cancer networks displayed a change in hubs, the most connected miRNAs. hsa-miR-103/106 were downgraded in cancer, whereas hsa-miR-30 became most prominent. Cancer networks appeared as built from disjointed subnetworks, as opposed to normal tissues. A comparison of these nets allowed us to identify key miRNA cliques in cancer. We also investigated miRNA copy number alterations in 744 cancer samples, at a resolution of 150 kb. Members of miRNA families should be similarly deleted or amplified, since they repress the same cellular targets and are thus expected to have similar impacts on oncogenesis. We correctly identified hsa-miR-17/92 family as amplified and the hsa-miR-143/145 cluster as deleted. Other miRNAs, such as hsa-miR-30 and hsa-miR-204, were found to be physically altered at the DNA copy number level as well. By combining differential expression, genetic networks, and DNA copy number alterations, we confirmed, or discovered, miRNAs with comprehensive roles in cancer. Finally, we experimentally validated the miRNA network with acute lymphocytic leukemia originated in Mir155 transgenic mice. Most of miRNAs deregulated in these transgenic mice were located close to hsa-miR-155 in the cancer network.
Subject(s)
Gene Expression Profiling , Gene Expression Regulation, Neoplastic , Leukemia , MicroRNAs/genetics , Neoplasms , Adenocarcinoma/metabolism , Animals , Cell Line, Tumor , Gene Dosage , Humans , Leukemia/genetics , Leukemia/metabolism , Lung/metabolism , Lung Neoplasms/metabolism , Mice , MicroRNAs/metabolism , Neoplasms/genetics , Neoplasms/metabolism , Oligonucleotide Array Sequence Analysis , Precursor Cell Lymphoblastic Leukemia-Lymphoma/geneticsABSTRACT
MOTIVATION: Microarray and other genome-wide technologies allow a global view of gene expression that can be used in several ways and whose potential has not been yet fully discovered. Functional insight into expression profiles is routinely obtained by using gene ontology terms associated to the cellular genes. In this article, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). We implemented this approach in a public web-based application named Fun&Co. By using Fun&Co, the user dissects in a pair-wise manner gene expression patterns and links correlated pairs to gene ontology terms. The proof of principle for our study was accomplished by dissecting molecular pathways in muscles. In particular, we identified specific cellular pathways by comparing the three different types of muscle in a pairwise fashion. In fact, we were interested in the specific molecular mechanisms regulating the cardiovascular system (cardiomyocytes and smooth muscle cells). RESULTS: We applied here Fun&Co to the molecular study of cardiovascular system and the identification of the specific molecular pathways in heart, skeletal and smooth muscles (using 317 microarrays) and to reveal functional differences between the three different kinds of muscle cells. AVAILABILITY: Application is online at http://tommy.unife.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.