Scientific Publications by FDA Staff
BMC Bioinformatics 2013 Dec 26;14(1):371
Identification of reproducible gene expression signatures in lung adenocarcinoma.
Lu TP, Chuang EY, Chen JJ
BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. Tremendous research efforts have been devoted to improving treatment procedures, but the average five-year overall survival rates are still less than 20%. Many biomarkers have been identified for predicting survival; challenges arise, however, in translating the findings into clinical practice due to their inconsistency and irreproducibility. In this study, we proposed an approach by identifying predictive genes through pathways. RESULTS: The microarrays from Shedden et al. were used as the training set, and the log-rank test was performed to select potential signature genes. We focused on 24 cancer-related pathways from 4 biological databases. A scoring scheme was developed by the Cox hazard regression model, and patients were divided into two groups based on the medians. Subsequently, their predictability and generalizability were evaluated by the 2-fold cross-validation and a resampling test in 4 independent datasets, respectively. A set of 16 genes related to apoptosis execution was demonstrated to have good predictability as well as generalizability in more than 700 lung adenocarcinoma patients and was reproducible in 4 independent datasets. This signature set was shown to have superior performances compared to 6 other published signatures. Furthermore, the corresponding risk scores derived from the set were found to associate with the efficacy of the anti-cancer drug ZD-6474 targeting EGFR. CONCLUSIONS: In summary, we presented a new approach to identify reproducible survival predictors for lung adenocarcinoma, and the identified genes may serve as both prognostic and predictive biomarkers in the future.
|Category: Journal Article|
|PubMed ID: #24369726||DOI: 10.1186/1471-2105-14-371|
|PubMed Central ID: #PMC3877965|
|Includes FDA Authors from Scientific Area(s): Toxicological Research|
|Entry Created: 2014-01-02||Entry Last Modified: 2014-02-09|