Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

Journal: Human genomics
PMID: 38840185

Abstract

BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology.

Authors

  • Jeffrey To
    Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada.
  • Soumita Ghosh
    Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada.
  • Xun Zhao
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Elisa Pasini
    Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada.
  • Sandra Fischer
    Department of Pathology, University Health Network, Toronto, ON, Canada.
  • Gonzalo Sapisochin
    Multi-Organ Transplant Program, University Health Network.
  • Anand Ghanekar
    Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada.
  • Elmar Jaeckel
    Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Division of Gastroenterology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
  • Mamatha Bhat
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.

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