Dataset Information

SNPs and Somatic Mutation on Long Non-Coding RNA: New Frontier in the Cancer Studies?

ABSTRACT: In the last decade, it has been demonstrated that long non-coding RNAs (lncRNAs) are involved in cancer development. The great majority of studies on lncRNAs report alterations, principally on their expression profiles, in several tumor types with respect to the normal tissues of origin. Conversely, since lncRNAs constitute a relatively novel class of RNAs compared to protein-coding transcripts (mRNAs), the landscape of their mutations and variations has not yet been extensively studied. However, in recent years an ever-increasing number of articles have described mutations of lncRNAs. Single-nucleotide polymorphisms (SNPs) that occur within the lncRNA transcripts can affect the structure and function of these RNA molecules, while the presence of a SNP in the promoter region of a lncRNA could alter its expression level. Also, somatic mutations that occur within lncRNAs have been shown to exert important effects in cancer and preliminary data are promising. Overall, the evidence suggests that SNPs and somatic mutation on lncRNAs may play a role in the pathogenesis of cancer, and indicates strong potential for further development of lncRNAs as biomarkers.

SUBMITTER: Minotti L

PROVIDER: S-EPMC6306726 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

SNPs and Somatic Mutation on Long Non-Coding RNA: New Frontier in the Cancer Studies?

Minotti Linda L Agnoletto Chiara C Baldassari Federica F Corrà Fabio F Volinia Stefano S

High-throughput 20181116 4

In the last decade, it has been demonstrated that long non-coding RNAs (lncRNAs) are involved in cancer development. The great majority of studies on lncRNAs report alterations, principally on their expression profiles, in several tumor types with respect to the normal tissues of origin. Conversely, since lncRNAs constitute a relatively novel class of RNAs compared to protein-coding transcripts (mRNAs), the landscape of their mutations and variations has not yet been extensively studied. However ...[more]

PMID: 30453571

Similar Datasets

Project description:BackgroundRenal cell carcinoma (RCC) is a malignant tumor with high morbidity and mortality. It is characterized by a large number of somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) are widely involved in the expression of genomic instability in renal cell carcinoma. But no studies have identified the genome instability-related lncRNAs (GInLncRNAs) and their clinical significances in RCC.MethodsClinical data, gene expression data and mutation data of 943 RCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the mutation data and lncRNA expression data, GInLncRNAs were screened out. Co-expression analysis, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore their potential functions and related signaling pathways. A prognosis model was further constructed based on genome instability-related lncRNAs signature (GInLncSig). And the efficiency of the model was verified by receiver operating characteristic (ROC) curve. The relationships between the model and clinical information, prognosis, mutation number and gene expression were analyzed using correlation prognostic analysis. Finally, the prognostic model was verified in clinical stratification according to TCGA dataset.ResultsA total of 45 GInLncRNAs were screened out. Functional analysis showed that the functional genes of these GInLncRNAs were mainly enriched in chromosome and nucleoplasmic components, DNA binding in molecular function, transcription and complex anabolism in biological processes. Univariate and Multivariate Cox analyses further screened out 11 GInLncSig to construct a prognostic model (AL031123.1, AC114803.1, AC103563.7, AL031710.1, LINC00460, AC156455.1, AC015977.2, 'PRDM16-dt', AL139351.1, AL035661.1 and LINC01606), and the coefficient of each GInLncSig in the model was calculated. The area under the curve (AUC) value of the ROC curve was 0.770. Independent analysis of the model showed that the GInLncSig model was significantly correlated with the RCC patients' overall survival. Furthermore, the GInLncSig model still had prognostic value in different subgroups of RCC patients.ConclusionOur study preliminarily explored the relationship between genomic instability, lncRNA and clinical characteristics of RCC patients, and constructed a GInLncSig model consisted of 11 GInLncSig to predict the prognosis of patients with RCC. At the same time, our study provided theoretical support for the exploration of the formation and development of RCC.

Project description:Neuroblastoma (NB) is an embryonal tumor with various clinical presentations and behaviors. Several genomic alterations has been well-studied in NB, among which genomic amplification of MYCN oncogene, is a strong prognostic biomarker with worsens outcome. Long noncoding RNAs (lncRNAs), constitute major proportion of the cellular transcripts with no coding capacity. One of their function is to guide transcription factors to the target genes and facilitate gene expression. However, relative contribution of lncRNA and MYCN to the advanced NB has remained unclear. Herein, by applying a network-based integrative analysis on MYCN amplified and MYCN nonamplified lncRNA expression profile from both RNA-seq and microarray platform, we identified lncRNA, SNHG1 to be differentially expressed and strongly correlated with MYCN in MYCN-amplified NB. The expression of SNHG1 was validated by RT-qPCR in NB cell lines. Survival analysis revealed that higher expression of SNHG1 significantly associates with poor patient survival. Moreover, knockdown of MYCN in MYCN-amplified NB cell lines inhibited SNHG1 expression. Furthermore, to unravel the role of SNHG1 in NB, we extracted SNHG1-interacting proteins by RNA-protein pull down assay coupled with doi:10.6342/NTU201701980 ! ! VI liquid chromatography-tandem mass spectrometry (LC-MS/MS). We identified 27 SNHG1-interacting proteins in common from three NB cell lines. However, only three SNHG1-interacting proteins, MATR3, YBX1 and HHRNPL have binding site detected by DeepBind motif analysis. Western blot confirms interaction of MATR3 with SNHG1. Additionally, we further validated the direct interaction between MATR3 and SNHG1 by RNA-immunoprecipation (IP). MATR3 is known to be involved in RNA transport and stabilization. Therefore, we proposed that MATR3 after interacting with SNHG1 might help in SNHG1 transcription and stabilization. In conclusion, our study unveils that SNHG1 could be a prognostic marker for high-risk NB and possibly stabilized by MATR3. Our results might provide future directions for the development of therapeutic strategies against high-risk NB.

Dataset Information

SNPs and Somatic Mutation on Long Non-Coding RNA: New Frontier in the Cancer Studies?

Publications

SNPs and Somatic Mutation on Long Non-Coding RNA: New Frontier in the Cancer Studies?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets