Predicting survival times for neuroblastoma patients using RNA-seq expression profiles

Biology Direct
Tyler GrimesSomnath Datta

Abstract

Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining these different profiles. The use of RNA-seq data is shown to improve accuracy in comparison to using clinical data alone for predicting overall survival times. Furthermore, clinically high-risk patients can be subclassified based on their predicted overall survival times. In this effort, the best performing model was the elastic net using both transcripts and introns together. This model separated patients into two groups with 2-year overall survival rates of 0.40±0.11 (n=22) versus 0.80±0.05 (n=68). The ensemble approach gave similar results, with groups 0.42±0.10 (n=25) versus 0.82±0.05 (n=65). This suggests that the ensemble is able to effectively combine the individual RNA-seq datasets. Using predicted survival times based on RNA-seq data can provide imp...Continue Reading

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Citations

Jan 3, 2019·Genomics & Informatics·Seokho JeongTaesung Park
Jun 22, 2019·Scientific Reports·Benjamin SzubertIgnat Drozdov
Jan 4, 2020·Genomics & Informatics·Seungyeoun Lee, Heeju Lim
Mar 20, 2019·Cancer Informatics·Peter UtnesChrister Einvik
Nov 18, 2020·Biochemistry. Biokhimii︠a︡·G Melino
Nov 21, 2020·Biology Direct·Ivana CelardoIvano Amelio
Dec 11, 2020·Cell Death Discovery·Ivano AmelioGerry Melino
Oct 16, 2020·Biology Direct·Ivano AmelioGerry Melino
Dec 11, 2020·Cell Death Discovery·Alessio ButeraGerry Melino
Jun 29, 2021·Frontiers in Cell and Developmental Biology·Yining XuYadong Wang

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Methods Mentioned

BETA
RNA-seq

Software Mentioned

R package
GeneCards Suite
elnet
spls
DAVID
GeneCards
lasso
R
survival
glmnet

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