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Prediction of platinum‐resistance patients of gastric cancer using bioinformatics
Author(s) -
Pan Jiaomeng,
Xiang Zhen,
Dai Qingqiang,
Wang Zhenqiang,
Liu Bingya,
Li Chen
Publication year - 2019
Publication title -
journal of cellular biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.028
H-Index - 165
eISSN - 1097-4644
pISSN - 0730-2312
DOI - 10.1002/jcb.28621
Subject(s) - cancer , platinum , resistance (ecology) , bioinformatics , computational biology , biology , medicine , biochemistry , catalysis , ecology
Lack of guidelines for personalized chemotherapy treatment after surgery has caused gastric cancer (GC) patients' unnecessary exposure to toxicity and the financial burden of chemotherapy treatments. In our study, we aimed to identify potential biomarkers to predict GC patients' susceptibility to platinum‐based on Gene Expression Omnibus (GEO) data sets. A total of 603 differentially expressed genes (DEGs) were identified between platinum‐resistant cell lines and platinum‐sensitive cell lines based on the Cancer Cell Line Encyclopedia (CCLE) data sets. A total of 253 patients who had accepted radical gastrectomy were recruited, of which 97 received platinum‐based chemotherapy and 156 were untreated. Three biomarkers (BRMS1, ND6, SRXN1) were then selected by univariate and multivariate Cox regression analysis to establish the predictive models using nomogram. Then this model was further validated through the GEO data set (GSE62254) which showed that this model could precisely predict the disease‐free survival and overall survival of patients treated with platinum‐based chemotherapy after surgery compared with untreated GC patients ( P < 0.0001). This predictive model might provide helpful messages about the patients' susceptibility to platinum to guide personalized chemotherapy.