Discovery of prognostic biomarkers for predicting @entity2 XXXX using microarray and survival data.

@entity1 @entity5 @entity2 @entity72136 @entity3 @entity3023
BACKGROUND: Few studies have investigated prognostic biomarkers of distant metastases of @entity2 . One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of @entity5 cells causes @entity3 , which is detrimental to @entity1 ' survival. RESULTS: This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant @entity3 of @entity2 using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant @entity3 and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight @entity1 with @entity2 and a follow-up time of 120  months are used to identify a set of gene markers and an independent cohort of 26 @entity1 is used to evaluate the identified biomarkers. The medical records of the 78 @entity1 show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant- @entity3 @entity1 . The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as @entity3023 and @entity72136 , can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99  months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. CONCLUSIONS: The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.
