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Integrative analysis reveals driver long non-coding RNAs in osteosarcoma.

Zhenguo Luo | Li Xiao | Jing Li | Buhuai Dong | Chunsheng Wang
Medicine | 2019

Transcriptome profiling of osteosarcoma (OS) by next generation sequencing technology (NGS) has been broadly performed by previous researches, which uncovers a large number protein-coding driver genes, facilitates our understanding of the molecular mechanisms of OS formation, progression and metastasis. Recently, more and more researchers realize the importance of long non-coding RNAs (lncRNAs) on the development of OS. However, few studies focus on discovering driver lncRNAs.Here we collected somatic copy number alterations (SCNAs) and gene expression profiles of 84 samples from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. The RNA sequencing data detected 13,903 expressed lncRNAs, 157 of which were previously reported to be associated with cancer based on the annotations from Lnc2Cancer database.By analyzing the SNP array data, several significant SCNAs were detected, such as the amplifications on chromosomes 1q, 4q, 17p, 17q, and 19q, and deletions on 1q, 3q, 9p, 10q, and 15q. With the SCNA and gene expression profiles, we identified 167 driver genes by integrative analysis, including 162 novel driver lncRNAs, 2 lncRNAs reported to be associated with OS, and another 3 associated with other cancers. Furthermore, functional characterization and survival analysis revealed that RP11-241F15.10 may function as a tumor suppressor in OS, and loss of function may contribute to activation of Wnt signaling pathway.This study not only facilitates our understanding of the oncogenic or tumor-suppressor role of lncRNAs in OS, but also provides potential therapies for the patients with OS with metastasis or relapse.

Pubmed ID: 30732148

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GenePattern (tool)

RRID:SCR_003201

A powerful genomic analysis platform that provides access to hundreds of tools for gene expression analysis, proteomics, SNP analysis, flow cytometry, RNA-seq analysis, and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research.

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