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. 2013 May 2;8(5):e63494.
doi: 10.1371/journal.pone.0063494. Print 2013.

An ensemble prognostic model for colorectal cancer

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An ensemble prognostic model for colorectal cancer

Bi-Qing Li et al. PLoS One. .

Abstract

Colorectal cancer can be grouped into Dukes A, B, C, and D stages based on its developments. Generally speaking, more advanced patients have poorer prognosis. To integrate progression stage prediction systems with recurrence prediction systems, we proposed an ensemble prognostic model for colorectal cancer. In this model, each patient was assigned a most possible stage and a most possible recurrence status. If a patient was predicted to be recurrence patient in advanced stage, he would be classified into high risk group. The ensemble model considered both progression stages and recurrence status. High risk patients and low risk patients predicted by the ensemble model had a significant different disease free survival (log-rank test p-value, 0.0016) and disease specific survival (log-rank test p-value, 0.0041). The ensemble model can better distinguish the high risk and low risk patients than the stage prediction model and the recurrence prediction model alone. This method could be applied to the studies of other diseases and it could significantly improve the prediction performance by ensembling heterogeneous information.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. IFS curve showing the overall prediction accuracies versus gene numbers for CRC stages prediction model.
The IFS curves were drawn based on the data in File S2. The overall prediction accuracy reached the peak when the number of genes was 25. The 25 genes thus obtained were used to compose the optimal gene set for the CRC stage predictor.
Figure 2
Figure 2. IFS curve showing the MCC versus gene numbers for CRC recurrence prediction model.
The IFS curves were drawn based on the data in File S4. The MCC reached the peak when the number of genes was 110. The 110 genes thus obtained were used to compose the optimal gene set for the CRC recurrence predictor.
Figure 3
Figure 3. Survival curve for the first proposal.
(A) Disease free survival (log-rank test, p-value = 0.0016). (B) Disease specific survival (log-rank test, p-value = 0.0041).
Figure 4
Figure 4. Protein-protein interaction network between stage related genes and recurrence related genes.
Yellow round rectangles represent stage related genes while red elipses represent recurrence related genes. 20 of the 25 stage related genes and 61 of the 110 recurrence related genes were presented.

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