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Plasma microRNA‐based signatures to predict 3‐year postoperative recurrence risk for stage II and III gastric cancer
Author(s) -
Liu Xinyang,
Zhang Xiaowei,
Zhang Zhe,
Chang Jinjia,
Wang Zhichao,
Wu Zheng,
Wang Chenchen,
Sun Zuojun,
Ge Xiaoxiao,
Geng Ruixuan,
Tang Wenbo,
Dai Congqi,
Lin Ying,
Lin Fengjuan,
Sun Menghong,
Jia Weihua,
Xue Wenqiong,
Ji Jiafu,
Hu Ying,
Qin Guoyou,
Li Jin
Publication year - 2017
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.30895
Subject(s) - taqman , logistic regression , receiver operating characteristic , microrna , medicine , oncology , proportional hazards model , stage (stratigraphy) , adjuvant therapy , cancer , survival analysis , real time polymerase chain reaction , biology , gene , paleontology , biochemistry
Our aim was to identify plasma microRNA (miRNA)‐based signatures to predict 3‐year postoperative recurrence risk for patients with stage II and III gastric cancer (GC), so as to provide insights for individualized adjuvant therapy. Plasma miRNA expression was investigated in three phases, involving 407 patients recruited from three centers. ABI miRNA microarray and TaqMan Low Density Array were adopted in the discovery phase to identify potential miRNAs. Quantitative reverse‐transcriptase polymerase chain reaction was used to assess the expression of selected miRNAs. Logistic regression models were constructed in the training set ( n = 170) and validated in the validation set ( n = 169). Receiver operating characteristic analyses, survival analyses and subgroup analyses were further used to assess the accuracy of the models. We identified a 7 miRNA classifier and 7miR + pathological factors index that provided high predictive accuracy of GC recurrence (area under the curve = 0.725 and 0.841 in the training set; and 0.627 and 0.771 in the validation set). High‐risk patients defined by the signatures had significantly shorter disease‐free survival and overall survival than low‐risk patients. The 7 miRNA classifier is an independent prognostic factor, and could add predictive value to traditional prognostic factors. Subgroup analyses revealed the satisfactory performance persisted regardless of stage, and the two models both displayed high accuracy in stage IIA patients. In conclusion, identified microRNA signature may potentially provide some additional benefit for prediction of disease recurrence in patients with stage II and III GC.