Analyzing Breast Cancer Survival with Biomarkers
Author Information
Author(s): Angela P Presson, Nam K Yoon, Lora Bagryanova, Vei Mah, Mohammad Alavi, Erin L Maresh, Ayyappan K Rajasekaran, Lee Goodglick, David Chia, Steve Horvath
Primary Institution: UCLA
Hypothesis
Can breast cancer patients be clustered into distinct prognostic groups using tissue microarray data?
Conclusion
The study identifies three distinct patient groups based on tumor markers that correlate with different mortality rates.
Supporting Evidence
- The study identified three patient groups with low (5.4%), moderate (22%), and high (50%) mortality rates.
- WGCNA* was validated in two independent gene expression data sets.
- The WGCNA method outperformed traditional Cox regression in predicting survival.
Takeaway
Doctors can use certain markers in breast cancer patients to predict how likely they are to survive. This helps in deciding the best treatment.
Methodology
Weighted correlation network analysis (WGCNA) was applied to tissue microarray data from 82 breast cancer patients to identify prognostic groups.
Potential Biases
The WGCNA groups may be overfitted to the data due to the small sample size.
Limitations
The study was based on a single TMA data set and the three marker rule needs validation in other data sets.
Participant Demographics
The study included 82 breast cancer patients, with various histologic types and clinical stages.
Statistical Information
P-Value
3.9 × 10-4
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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