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C. elegans Germline as Three Distinct Tumor Models.

Mariah JonesMina NormanAlex Minh TietJiwoo LeeMyon-Hee Lee
Published in: Biology (2024)
Tumor cells display abnormal growth and division, avoiding the natural process of cell death. These cells can be benign (non-cancerous growth) or malignant (cancerous growth). Over the past few decades, numerous in vitro or in vivo tumor models have been employed to understand the molecular mechanisms associated with tumorigenesis in diverse regards. However, our comprehension of how non-tumor cells transform into tumor cells at molecular and cellular levels remains incomplete. The nematode C. elegans has emerged as an excellent model organism for exploring various phenomena, including tumorigenesis. Although C. elegans does not naturally develop cancer, it serves as a valuable platform for identifying oncogenes and the underlying mechanisms within a live organism. In this review, we describe three distinct germline tumor models in C. elegans , highlighting their associated mechanisms and related regulators: (1) ectopic proliferation due to aberrant activation of GLP-1/Notch signaling, (2) meiotic entry failure resulting from the loss of GLD-1/STAR RNA-binding protein, (3) spermatogenic dedifferentiation caused by the loss of PUF-8/PUF RNA-binding protein. Each model requires the mutations of specific genes ( glp-1 , gld-1 , and puf-8 ) and operates through distinct molecular mechanisms. Despite these differences in the origins of tumorigenesis, the internal regulatory networks within each tumor model display shared features. Given the conservation of many of the regulators implicated in C. elegans tumorigenesis, it is proposed that these unique models hold significant potential for enhancing our comprehension of the broader control mechanisms governing tumorigenesis.
Keyphrases
  • binding protein
  • cell death
  • signaling pathway
  • squamous cell carcinoma
  • induced apoptosis
  • dna repair
  • genome wide
  • dna methylation
  • climate change
  • papillary thyroid
  • oxidative stress
  • nucleic acid
  • bioinformatics analysis