Dataset Information

Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer.

ABSTRACT: Next-generation sequencing is increasing our understanding and knowledge of non-coding RNAs (ncRNAs), elucidating their roles in molecular mechanisms and processes such as cell growth and development. Within such a class, tRNA-derived ncRNAs have been recently associated with gene expression regulation in cancer progression. In this paper, we characterize, for the first time, tRNA-derived ncRNAs in NCI-60. Furthermore, we assess their expression profile in The Cancer Genome Atlas (TCGA). Our comprehensive analysis allowed us to report 322 distinct tRNA-derived ncRNAs in NCI-60, categorized in tRNA-derived fragments (11 tRF-5s, 55 tRF-3s), tRNA-derived small RNAs (107 tsRNAs) and tRNA 5' leader RNAs (149 sequences identified). In TCGA, we were able to identify 232 distinct tRNA-derived ncRNAs categorized in 53 tRF-5s, 58 tRF-3s, 63 tsRNAs and 58 5' leader RNAs. This latter group represents an additional evidence of tRNA-derived ncRNAs originating from the 5' leader region of precursor tRNA. We developed a public database, tRFexplorer, which provides users with the expression profile of each tRNA-derived ncRNAs in every cell line in NCI-60 as well as for each TCGA tumor type. Moreover, the system allows us to perform differential expression analyses of such fragments in TCGA, as well as correlation analyses of tRNA-derived ncRNAs expression in TCGA and NCI-60 with gene and miRNA expression in TCGA samples, in association with all omics and compound activities data available on CellMiner. Hence, the tool provides an important opportunity to investigate their potential biological roles in absence of any direct experimental evidence. Database URL: https://trfexplorer.cloud/.

SUBMITTER: La Ferlita A

PROVIDER: S-EPMC6859256 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer.

La Ferlita Alessandro A Alaimo Salvatore S Veneziano Dario D Nigita Giovanni G Balatti Veronica V Croce Carlo M CM Ferro Alfredo A Pulvirenti Alfredo A

Database : the journal of biological databases and curation 20190101

Next-generation sequencing is increasing our understanding and knowledge of non-coding RNAs (ncRNAs), elucidating their roles in molecular mechanisms and processes such as cell growth and development. Within such a class, tRNA-derived ncRNAs have been recently associated with gene expression regulation in cancer progression. In this paper, we characterize, for the first time, tRNA-derived ncRNAs in NCI-60. Furthermore, we assess their expression profile in The Cancer Genome Atlas (TCGA). Our com ...[more]

PMID: 31735953

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Project description:Nullomer peptides are the smallest sequences absent from databases of natural proteins. We first began compiling a list of absent 5-amino acid strings in 2006 (1). We report here the effects of Nullomer-derived peptides 9R, 9S1R and 124R on the NCI-60 panel, derived from human cancers of 9 organs (kidney, ovary, skin melanoma, lung, brain, lung, colon, prostate and the hematopoietic system), and four normal cell lines (endothelial HUVEC, skin fibroblasts BJ, colon epithelial FHC and normal prostate RWPE-1).NCI-60 cancer cell panel and four normal cell lines were cultured in vitro in RPMI1640 supplemented with 10% Hyclone fetal bovine serum and exposed for 48 h to 5 ?M, 25 ?M and 50 ?M of peptides 9R, 9S1R and 124R. Viability was assessed by CCK-8 assay. For peptide ATP depletion effects, one cell line representing each organ in the NCI-60 panel, and four normal cell lines were exposed to 50 ?M of peptides 9R, 9S1R and 124R for 3 h. The ATP content was assessed in whole cells, and their supernatants.Peptides 9S1R and 9R are respectively lethal to 95 and 81.6% of the 60 cancer cell lines tested. Control peptide 124R has no effect on the growth of these cells. Especially interesting the fact that peptides 9R and 9S1R are capable of killing drug-resistant and hormone-resistant cell lines, and even cancer stem cells. Peptides 9R and 9S1R have a broader activity spectrum than many cancer drugs in current use, can completely deplete cellular ATP within 3 h, and are less toxic to 3 of the 4 normal cell lines tested than they are to several cancers.Nullomer peptides 9R and 9S1R have a large broad lethal effect on cancer cell lines derived from nine organs represented in the NCI-60 panel. This broad activity crosses many of the categorical divisions used in the general classification of cancers: solid vs liquid cancers, drug sensitive vs drug resistant, hormone sensitive vs hormone resistant, cytokine sensitive vs cytokine non sensitive, slow growing vs rapid growing, differentiated vs dedifferentiated cancers. Furthermore peptides 9R and 9S1R are lethal to cancer stem cells and breast canrcinosarcoma.

Project description:BackgroundCancer is a complex disease where various types of molecular aberrations drive the development and progression of malignancies. Large-scale screenings of multiple types of molecular aberrations (e.g., mutations, copy number variations, DNA methylations, gene expressions) become increasingly important in the prognosis and study of cancer. Consequently, a computational model integrating multiple types of information is essential for the analysis of the comprehensive data.ResultsWe propose an integrated modeling framework to identify the statistical and putative causal relations of various molecular aberrations and gene expressions in cancer. To reduce spurious associations among the massive number of probed features, we sequentially applied three layers of logistic regression models with increasing complexity and uncertainty regarding the possible mechanisms connecting molecular aberrations and gene expressions. Layer 1 models associate gene expressions with the molecular aberrations on the same loci. Layer 2 models associate expressions with the aberrations on different loci but have known mechanistic links. Layer 3 models associate expressions with nonlocal aberrations which have unknown mechanistic links. We applied the layered models to the integrated datasets of NCI-60 cancer cell lines and validated the results with large-scale statistical analysis. Furthermore, we discovered/reaffirmed the following prominent links: (1) Protein expressions are generally consistent with mRNA expressions. (2) Several gene expressions are modulated by composite local aberrations. For instance, CDKN2A expressions are repressed by either frame-shift mutations or DNA methylations. (3) Amplification of chromosome 6q in leukemia elevates the expression of MYB, and the downstream targets of MYB on other chromosomes are up-regulated accordingly. (4) Amplification of chromosome 3p and hypo-methylation of PAX3 together elevate MITF expression in melanoma, which up-regulates the downstream targets of MITF. (5)Mutations of TP53 are negatively associated with its direct target genes.ConclusionsThe analysis results on NCI-60 data justify the utility of the layered models for the incoming flow of cancer genomic data. Experimental validations on selected prominent links and application of the layered modeling framework to other integrated datasets will be carried out subsequently.

Dataset Information

Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer.

Publications

Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer.

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