Cancer subtypes classification using long non-coding RNA

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

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Oncotarget. 2016; 7:54082-54093. https://doi.org/10.18632/oncotarget.10213

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Ronan Flippot1,*, Gabriel G. Malouf1,*, Xiaoping Su2, Roger Mouawad1, Jean-Philippe Spano1 and David Khayat1

1 Groupe Hospitalier Pitié-Salpêtrière, Department of Medical Oncology, University Pierre and Marie Curie (Paris VI), Institut Universitaire de Cancérologie, AP-HP, Paris, France

2 Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA

* The authors have contributed equally as co-first authors

Correspondence to:

Gabriel G. Malouf, email:

Keywords: long non-coding RNAs, lncRNAs, cancer, classification, prognosis

Received: November 08, 2015 Accepted: May 30, 2016 Published: June 21, 2016


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.

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