Prognostic Gene Signatures of Liver Malignancy
2011

Gene Signatures from a Liver Cancer Mouse Model Predict Survival in Patients

Sample size: 293 publication 10 minutes Evidence: high

Author Information

Author(s): Ivanovska Irena, Zhang Chunsheng, Liu Angela M., Wong Kwong F., Lee Nikki P., Lewis Patrick, Philippar Ulrike, Bansal Dimple, Buser Carolyn, Scott Martin, Mao Mao, Poon Ronnie T. P., Fan Sheung Tat, Cleary Michele A., Luk John M., Dai Hongyue

Primary Institution: Merck & Co., Inc.

Hypothesis

The study aims to analyze the molecular signatures of liver cancer in a c-MET-transgenic mouse model and investigate its prognostic relevance to human hepatocellular carcinoma (HCC).

Conclusion

The study provides evidence that a disease model can serve as a possible platform for generating hypotheses to be tested in human tissues and highlights an efficient method for generating biomarker signatures before extensive clinical trials.

Supporting Evidence

  • The study identified gene signatures that were down-regulated in both mouse tumors and human HCC, which had significant predictive power on overall and disease-free survival.
  • Mouse tumors showed parallels with human liver tumors, including down-regulation of metabolic pathways.
  • The predictive power of the mouse-derived signatures stems from their tumor properties rather than c-MET-driven properties.

Takeaway

Scientists studied mice with liver cancer to find patterns that could help predict how long patients with liver cancer might live.

Methodology

The study involved molecular profiling of liver tissues from a c-MET-driven mouse model and human HCC samples, using whole genome microarray expression profiling.

Potential Biases

Potential bias due to the reliance on a single mouse model and the limited number of human samples.

Limitations

The predictive power of the mouse-derived signatures may not fully represent human disease due to differences in tumor initiation mechanisms.

Participant Demographics

272 HBV-associated and 9 HCV-associated HCC patients.

Statistical Information

P-Value

6.3×10−6

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.1371/journal.pone.0024582

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