FAM84B promotes prostate tumorigenesis through a network alteration Academic Article uri icon

  •  
  • Overview
  •  
  • Research
  •  
  • Identity
  •  
  • Additional Document Info
  •  
  • View All
  •  

abstract

  • Background: The aim of this study was to investigate the contributions of FAM84B in prostate tumorigenesis and progression. Methods: A FAM84B mutant with deletion of its HRASLS domain (ΔHRASLS) was constructed. DU145 prostate cancer (PC) cells stably expressing an empty vector (EV), FAM84B, or FAM84B (ΔHRASLS) were produced. These lines were examined for proliferation, invasion, and growth in soft agar in vitro. DU145 EV and FAM84B cells were investigated for tumor growth and lung metastasis in NOD/SCID mice. The transcriptome of DU145 EV xenografts (n = 2) and DU145 FAM84B tumors (n = 2) was determined using RNA sequencing, and analyzed for pathway alterations. The FAM84B-affected network was evaluated for an association with PC recurrence. Results: FAM84B but not FAM84B (ΔHRASLS) increased DU145 cell invasion and growth in soft agar. Co-immunoprecipitation and co-localization analyses revealed an interaction between FAM84B and FAM84B (ΔHRASLS), suggesting an intramolecular association among FAM84B molecules. FAM84B significantly enhanced DU145 cell-derived xenografts and lung metastasis. In comparison with DU145 EV cell-produced tumors, those generated by DU145 FAM84B cells showed a large number of differentially expressed genes (DEGs; n = 4976). A total of 51 pathways were enriched in these DEGs, which function in the Golgi-to-endoplasmic reticulum processes, cell cycle checkpoints, mitochondrial events, and protein translation. A novel 27-gene signature (SigFAM) was derived from these DEGs; SigFAM robustly stratifies PC recurrence in two large PC populations (n = 490, p = 0; n = 140, p = 4e-11), and remains an independent risk factor of PC recurrence after adjusting for age at diagnosis, Gleason scores, surgical margin, and tumor stages. Conclusions: FAM84B promotes prostate tumorigenesis through a complex network that predicts PC recurrence.

authors

  • Jiang, Yanzhi
  • Lin, Xiaozeng
  • Kapoor, Anil
  • He, Lizhi
  • Wei, Fengxiang
  • Gu, Yan
  • Mei, Wenjuan
  • Zhao, Kuncheng
  • Yang, Huixiang
  • Tang, Damu

publication date

  • January 2019