A method for analyzing censored survival phenotype with gene expression data.
BACKGROUND
Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles and survival time. However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study.


METHODS
We propose a method for an integrated study of survival time and gene expression. This method can be summarized as a two-step procedure: in the first step, a moderate number of genes are pre-selected using correlation or liquid association (LA). Imputation and transformation methods are employed for the correlation/LA calculation. In the second step, the dimension of the predictors is further reduced using the modified sliced inverse regression for censored data (censorSIR).


RESULTS
The new method is tested via both simulated and real data. For the real data application, we employed a set of 295 breast cancer patients and found a linear combination of 22 gene expression profiles that are significantly correlated with patients' survival rate.


CONCLUSIONS
By an appropriate combination of feature selection and dimension reduction, we find a method of identifying gene expression signatures which is effective for survival prediction.
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA. ttwu@umd.edu
Tongtong
T
Wu
Wei
W
Sun
Shinsheng
S
Yuan
Chun-Houh
CH
Chen
Ker-Chau
KC
Li
D014408:Biomarkers, Tumor / Q000235:genetics; D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000235:genetics* / Q000401:mortality*; D005260:Female; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D053208:Kaplan-Meier Estimate; D025341:Principal Component Analysis; D012044:Regression Analysis; D015996:Survival Rate
D014408:Biomarkers, Tumor