Cancer subtypes classification using long non-coding RNA

Ronan Flippot (1, 2) , Gabriel G. Malouf (2, 1) , Xiaoping Su (3) , Roger Mouawad (2, 1) , Jean-Philippe Spano (2, 1) , David Khayat (2, 1)

1 UPMC - Université Pierre et Marie Curie - Paris 6
2 CHU Pitié-Salpêtrière [AP-HP]
3 Department of Bioinformatics and Computational Biology

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.

Mots clés: prognosis; cancer; classification; long non-coding RNAs; lncRNAs

Domaines: Cancer

Ronan Flippot, Gabriel G. Malouf, Xiaoping Su, Roger Mouawad, Jean-Philippe Spano, et al.. Cancer subtypes classification using long non-coding RNA. Oncotarget, 2016, 7 (33), pp.54082-54093. ⟨10.18632/oncotarget.10213⟩. ⟨hal-01419223⟩