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. 2011 Apr 7;17(13):1710-7.
doi: 10.3748/wjg.v17.i13.1710.

Discovery and validation of prognostic markers in gastric cancer by genome-wide expression profiling

Yue-Zheng Zhang  1 , Lian-Hai Zhang, Yang Gao, Chao-Hua Li, Shu-Qin Jia, Ni Liu, Feng Cheng, De-Yun Niu, William Cs Cho, Jia-Fu Ji, Chang-Qing Zeng
Affiliations

Discovery and validation of prognostic markers in gastric cancer by genome-wide expression profiling

Yue-Zheng Zhang et al. World J Gastroenterol. .

Abstract

Aim: To develop a prognostic gene set that can predict patient overall survival status based on the whole genome expression analysis.

Methods: Using Illumina HumanWG-6 BeadChip followed by semi-supervised analysis, we analyzed the expression of 47,296 transcripts in two batches of gastric cancer patients who underwent surgical resection. Thirty-nine samples in the first batch were used as the training set to discover candidate markers correlated to overall survival, and thirty-three samples in the second batch were used for validation.

Results: A panel of ten genes were identified as prognostic marker in the first batch samples and classified patients into a low- and a high-risk group with significantly different survival times (P = 0.000047). This prognostic marker was then verified in an independent validation sample batch (P = 0.0009). By comparing with the traditional Tumor-node-metastasis (TNM) staging system, this ten-gene prognostic marker showed consistent prognosis results. It was the only independent prognostic value by multivariate Cox regression analysis (P = 0.007). Interestingly, six of these ten genes are ribosomal proteins, suggesting a possible association between the deregulation of ribosome related gene expression and the poor prognosis.

Conclusion: A ten-gene marker correlated with overall prognosis, including 6 ribosomal proteins, was identified and verified, which may complement the predictive value of TNM staging system.

Keywords: Gastric cancer; Gene expression profiling; Prognosis; Ribosomal proteins; Survival markers.

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Figures

Figure 1
Figure 1
Overview of the strategy used for the development and validation of prognostic markers.
Figure 2
Figure 2
Overall survival curves and the expression profile of the ten-gene prognostic marker in the training dataset. A: The gene expression pattern of the ten-gene prognostic marker. Nine genes were associated with the prolonged survival and one gene with poor survival. Red, high expression; green, low expression; B: Kaplan-Meier survival curves based on the expression profile of the ten-gene prognostic marker; C: Overall survival curves according to the tumor-node-metastasis stages.
Figure 3
Figure 3
Overall survival curves and the expression profile of the ten-gene prognostic marker in the validation dataset. A: The gene expression pattern of the ten-gene prognostic marker. Red, high expression; green, low expression; B: Kaplan-Meier survival curves based on the expression profile of the ten-gene prognostic marker; C: Overall survival curves according to the tumor-node-metastasis stages.
See this image and copyright information in PMC

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