Reference-free transcriptome exploration reveals novel RNAs for prostate cancer diagnosis

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 un...

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Bibliographic Details
Published in:Life science alliance 2019-12, Vol.2 (6), p.e201900449
Main Authors: Pinskaya, Marina, Saci, Zohra, Gallopin, Mélina, Gabriel, Marc, Nguyen, Ha Tn, Firlej, Virginie, Descrimes, Marc, Rapinat, Audrey, Gentien, David, Taille, Alexandre de la, Londoño-Vallejo, Arturo, Allory, Yves, Gautheret, Daniel, Morillon, Antonin
Format: Article
Language:English
Subjects:
Cancer
Life Sciences
Methods
Quantitative Methods
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Summary: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.
ISSN:2575-1077
2575-1077
DOI:10.26508/lsa.201900449