Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer

Abstract Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill this gap, we present a deep learning (DL)–based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We built the DL-based, survival-sensitive model on 360 HCC patients' data using RNA sequencing (RNA-Seq), miRNA sequencing (miRNA-Seq), and methylation data from The Cancer Genome Atlas (TCGA), which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL-based…

• HC
  Hawaii Community Foundation

  Award: 14ADVC-64566
• NI
  National Institutes of Health

  Award: P20 COBRE GM103457
• NI
  National Institute of Environmental Health Sciences

  Award: K01ES025434
• NI
  National Institute of Child Health and Human Development

  Award: R01 HD084633
• UN
  U.S. National Library of Medicine

  Award: R01LM012373