Gene Signature for Breast Cancer Prognosis
Author Information
Author(s): Liu Jiangang, Campen Andrew, Huang Shuguang, Peng Sheng-Bin, Ye Xiang, Palakal Mathew, Dunker A Keith, Xia Yuni, Li Shuyu
Primary Institution: Lilly Research Laboratories, Eli Lilly and Company
Hypothesis
Can gene expression profiling in the cell cycle pathway improve breast cancer prognosis predictions?
Conclusion
The cell cycle gene signature model is a powerful and accurate predictor of breast cancer outcomes.
Supporting Evidence
- The cell cycle gene signature was validated in multiple independent datasets.
- The model outperformed the previously established Amsterdam 70-gene signature.
- Differential gene expression in the cell cycle pathway correlated with patient survival.
Takeaway
Scientists found that looking at certain genes related to the cell cycle can help predict how breast cancer will behave in patients.
Methodology
The study used unsupervised clustering and Kaplan-Meier survival analysis on multiple gene expression datasets.
Limitations
The study did not examine the stability of the gene signature across different datasets.
Participant Demographics
The study analyzed data from 1,162 breast cancer patients across multiple datasets.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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