Open AccessPrognostic value of a 25-gene assay in patients with gastric cancer after curative resection
Author(s) -
Xiaohong Wang,
Yiqiang Liu,
Zhaojian Niu,
Runjia Fu,
Yongning Jia,
Li Zhang,
DuanFang Shao,
Hong Du,
Hanjie Ying,
Xiaofang Xing,
Xiaoxing Cheng,
Lin Li,
Ting Guo,
Ziyu Li,
Qunsheng Ji,
Lianhai Zhang,
Jiafu Ji
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/s41598-017-07604-y
Subject(s) - medicine , oncology , stage (stratigraphy) , adjuvant chemotherapy , chemotherapy , microarray , cohort , cancer , gene signature , gene , biology , gene expression , breast cancer , paleontology , biochemistry
This study aimed to develop and validate a practical, reliable assay for prognosis and chemotherapy benefit prediction compared with conventional staging in Gastric cancer (GC). Twenty-three candidate genes with significant correlation between quantitative hybridization and microarray results plus 2 reference genes were selected to form a 25-gene prognostic classifier, which can classify patients into 3 distinct groups of different risk of mortality obtained by analyzing microarray data from 78 frozen tumor specimens. The 25-gene assay was associated with overall survival in both training ( P = 0.017) and testing cohort ( P = 0.005) (462 formalin-fixed paraffin-embedded samples). The risk prediction in stages I + II is significantly better than that in stages III. Analysis demonstrated that this 25-gene signature is an independent prognostic predictor and show higher prognostic accuracy than conventional TNM staging in early stage patients. Moreover, only high-risk patients in stage I + II were found benefit from adjuvant chemotherapy ( P = 0.043), while low-risk patients in stage III were not found benefit from adjuvant chemotherapy. In conclusion, our results suggest that this 25-gene assay can reliably identify patients with different risk for mortality after surgery, especially for stage I + II patients, and might be able to predict patients who benefit from chemotherapy.