Paper Info
Title
Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication.
Abstract
Lung cancer is the most occurring cancer type, and its mortality rate is also the highest, among them lung adenocarcinoma (LUAD) accounts for about 40 % of lung cancer. There is an urgent need to develop a prognosis prediction model for lung adenocarcinoma. Previous LUAD prognosis studies only took single-omics data, such as mRNA or miRNA, into consideration. To this end, we proposed a deep learning-based autoencoding approach for combination of four-omics data, mRNA, miRNA, DNA methylation and copy number variations, to construct an autoencoder model, which learned representative features to differentiate the two optimal patient subgroups with a significant difference in survival (P = 4.08e-09) and good consistency index (C-index = 0.65). The multi-omics model was validated though four independent datasets, i.e. GSE81089 for mRNA (n = 198, P = 0.0083), GSE63805 for miRNA (n = 32, P = 0.018), GSE63384 for DNA methylation (n = 35, P = 0.009), and TCGA independent samples for copy number variations (n = 94, P = 0.0052). Finally, a functional analysis was performed on two survival subgroups to discover genes involved in biological processes and pathways. This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.
Year
DOI
Venue
2020
10.1016/j.compbiolchem.2020.107277
Computational Biology and Chemistry
Keywords
DocType
Volume
Lung adenocarcinoma,Multi-omics,Autoencoder,Deep learning,Machine learning,Survival analysis,Prognosis prediction
Journal
87
ISSN
Citations 
PageRank 
1476-9271
1
0.36
References 
Authors
0
5
Name
Order
Citations
PageRank
Tzong-Yi Lee161737.18
Kai-Yao Huang210.36
Cheng-Hsiang Chuang310.36
Cheng-Yang Lee410.36
Tzu-Hao Chang514511.92