Reference-free transcriptome signatures for prostate cancer prognosis
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Published:2021-04-12
Issue:1
Volume:21
Page:
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ISSN:1471-2407
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Container-title:BMC Cancer
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language:en
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Short-container-title:BMC Cancer
Author:
Nguyen Ha T.N.,Xue Haoliang,Firlej Virginie,Ponty Yann,Gallopin Melina,Gautheret Daniel
Abstract
Abstract
Background
RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data.
Methods
In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset.
Results
We find that both gene-based and k-mer based classifiers had similarly high performances for risk prediction and a markedly lower performance for relapse prediction. Interestingly, the reference-free signatures included a set of sequences mapping to novel lncRNAs or variable regions of cancer driver genes that were not part of gene-based signatures.
Conclusions
Reference-free classifiers are thus a promising strategy for the identification of novel prognostic RNA biomarkers.
Funder
911 Scholarship Fund from the Vietnamese Government Agence Nationale de la Recherche
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Genetics,Oncology
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