RT Journal Article
SR Electronic
T1 Reference-free transcriptome exploration reveals novel RNAs for prostate cancer diagnosis
JF Life Science Alliance
JO Life Sci. Alliance
FD Life Science Alliance LLC
SP e201900449
DO 10.26508/lsa.201900449
VO 2
IS 6
A1 Marina Pinskaya
A1 Zohra Saci
A1 Mélina Gallopin
A1 Marc Gabriel
A1 Ha TN Nguyen
A1 Virginie Firlej
A1 Marc Descrimes
A1 Audrey Rapinat
A1 David Gentien
A1 Alexandre de la Taille
A1 Arturo Londoño-Vallejo
A1 Yves Allory
A1 Daniel Gautheret
A1 Antonin Morillon
YR 2019
UL https://www.life-science-alliance.org/content/2/6/e201900449.abstract
AB The use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens are restricted by the limited power of conventional reference-based protocols relying on unique and annotated transcripts. Here, we implemented a blind reference-free computational protocol, DE-kupl, to infer yet unreferenced RNA variations from total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into putative long noncoding (lnc)RNAs expressed in prostate cancer. Through filtering of 1,179 candidates, we defined 21 lncRNAs that were further validated by NanoString for robust tumor-specific expression in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling more than 90% of true-positive detections of cancer in an independent The Cancer Genome Atlas cohort. Remarkably, this clinical signature made of only nine unannotated lncRNAs largely outperformed PCA3, the only used prostate cancer lncRNA biomarker, in detection of high-risk tumors. This modular workflow is highly sensitive and can be applied to any pathology or clinical application.