Pan-cancer systematic identification of lncRNAs associated with cancer prognosis

PeerJ

article

Matthew Ung¹  Evelien Schaafsma¹  Daniel Mattox²  George L. Wang¹  Chao Cheng¹ 

[1] Department of Molecular and Systems Biology, Dartmouth College;Department of Computer Science, Dartmouth College;Department of Medicine, Baylor College of Medicine;The Institute for Clinical and Translational Research, Baylor College of Medicine;Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth

关键词: LncRNA;    Prognosis;    Microarray;    RNA-seq;    TCGA;   

DOI  :  10.7717/peerj.8797

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background The “dark matter” of the genome harbors several non-coding RNA species including Long non-coding RNAs (lncRNAs), which have been implicated in neoplasia but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. Methods In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. Results In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

【 授权许可】

CC BY