Cancer subtypes classification using long non-coding RNA

Ronan Flippot, Gabriel G. Malouf, Xiaoping Su, Roger Mouawad, Jean Philippe Spano, David Khayat

Research output: Contribution to journalReview articlepeer-review

38 Scopus citations

Abstract

Inter-tumor heterogeneity might explain divergent clinical evolution of cancers bearing similar pathological features. In the last decade, genomic has highly improved tumor subtypes classification through the identification of oncogenic or tumor suppressor drivers. In addition, epigenetics and long non-coding RNAs (lncRNAs) are emerging as new fields for investigation, which might also account for tumor heterogeneity. There is growing evidence that modifications of lncRNA expression profiles are involved in cancer progression through epigenetic regulation, activation of pro-oncogenic pathways and crosstalks with other RNA subtypes. Consequently, the study of lncRNA expression profile will be a key factor in the future for charting cancer subtype classifications as well as defining prognostic and progression biomarkers. Herein we discuss the interest of lncRNA as potent prognostic and predictive biomarkers, and provide a glimpse on the impact of emerging cancer subtypes classification based on lncRNAs.

Original languageEnglish (US)
Pages (from-to)54082-54093
Number of pages12
JournalOncotarget
Volume7
Issue number33
DOIs
StatePublished - Aug 1 2016

Keywords

  • Cancer
  • Classification
  • LncRNAs
  • Long non-coding RNAs
  • Prognosis

ASJC Scopus subject areas

  • Oncology

MD Anderson CCSG core facilities

  • Bioinformatics Shared Resource

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