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A one-dimensional parameter-free model for carcinogenesis in gene expression space.

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Cancer risk is modeled by gene expression changes, including small variations and large collective gene shifts. Large shifts accurately predict lifetime cancer risk and link it to infection protection mechanisms in tissues.

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Area of Science:

  • Genomics
  • Cancer Biology
  • Mathematical Modeling

Background:

  • Tissue states are defined by gene expression.
  • External factors and genetic changes can alter these states.
  • Understanding cancer risk requires modeling these dynamic changes.

Purpose of the Study:

  • To model tissue microstate transitions in gene expression space.
  • To estimate cancer risk based on the probability of transitioning to a tumor state.
  • To explore the relationship between cancer risk and tissue's infection defense.

Main Methods:

  • Modeling microstate displacements using combined small gene expression variations and large Levy jumps.
  • Analyzing collective gene variations to represent Levy jumps.
  • Calculating the probability of transitioning from normal to tumor gene expression regions.

Main Results:

  • A model combining small variations and large Levy jumps describes microstate displacements.
  • The Levy jump component of the model accurately describes lifetime cancer risk across 8 tissues.
  • A correlation was found between cancer risk and a tissue's resistance to infections.

Conclusions:

  • Gene expression dynamics, particularly large collective shifts, are crucial for understanding cancer risk.
  • The proposed model offers a novel way to estimate lifetime cancer risk.
  • Tissue's innate defense mechanisms may influence its susceptibility to cancer.