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Identification of a novel gene pairs signature in the prognosis of gastric cancer

Current prognostic signatures need to be improved in identifying high‐risk patients of gastric cancer (GC). Thus, we aimed to develop a reliable prognostic signature that could assess the prognosis risk in GC patients. Two microarray datasets of GSE662254 (n = 300, training set) and GSE15459 (n = 19...

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Published in:Cancer medicine (Malden, MA) MA), 2018-02, Vol.7 (2), p.344-350
Main Authors: Peng, Pai‐Lan, Zhou, Xiang‐Yu, Yi, Guo‐Dong, Chen, Peng‐Fei, Wang, Fan, Dong, Wei‐Guo
Format: Article
Language:English
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Summary:Current prognostic signatures need to be improved in identifying high‐risk patients of gastric cancer (GC). Thus, we aimed to develop a reliable prognostic signature that could assess the prognosis risk in GC patients. Two microarray datasets of GSE662254 (n = 300, training set) and GSE15459 (n = 192, test set) were included into analysis. Prognostic genes were screened to construct prognosis‐related gene pairs (PRGPs). Then, a penalized Cox proportional hazards regression model identified seven PRGPs, which constructed a prognostic signature and divided patients into high‐ and low‐risk groups according to the signature score. High‐risk patients showed a poorer prognosis than low‐risk patients in both the training set (hazard ratios [HR]: 6.086, 95% confidence interval [CI]: 4.341–8.533) and test set (1.773 [1.107–2.840]). The PRGPs signature also achieved a higher predictive accuracy (concordance index [C‐index]: 0.872, 95% CI: 0.846–0.897) than two existing molecular signatures (0.706 [0.667–0.744] for a 11‐gene signature and 0.684 [0.642–0.726] for a 24‐lncRNA signature) and TNM stage (0.764 [0.715–0.814]). In conclusion, our study identified a novel gene pairs signature in the prognosis of GC. We used a novel method to identify a prognostic signature in gastric cancer, which removed the batch effects. The signature also showed a better predictive accuracy than other prognostic signatures.
ISSN:2045-7634
2045-7634
DOI:10.1002/cam4.1303