J Cancer 2019; 10(13):2953-2960. doi:10.7150/jca.31120

Research Paper

Three Biomarkers Predict Gastric Cancer Patients' Susceptibility To Fluorouracil-based Chemotherapy

Jiaomeng Pan*, Qingqiang Dai*, Zhen Xiang, Bingya Liu, Chen Li

Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, People's Republic of China.
*These authors contributed equally to this work.

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Citation:
Pan J, Dai Q, Xiang Z, Liu B, Li C. Three Biomarkers Predict Gastric Cancer Patients' Susceptibility To Fluorouracil-based Chemotherapy. J Cancer 2019; 10(13):2953-2960. doi:10.7150/jca.31120. Available from http://www.jcancer.org/v10p2953.htm

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Abstract

Background: Fluorouracil-based chemotherapy is recommended by the main clinical guidelines for post-operative gastric cancer (GC) patient's chemotherapy treatment, this study aim to establish relate model to predict patients' susceptibility to fluorouracil-based chemotherapy to prevent patients' unnecessary exposure to chemotherapy treatments and improve patients' treatment.

Methods: Data from Gene Expression Omnibus (GEO) database, Cancer Cell Line Encyclopedia (CCLE) database, Cancer Therapeutics Response Portal (CTRP) and The Cancer Genome Atlas (TCGA) were used. A predictive model was built based on univariate and multivariate Cox analysis and visualized by nomogram. Survival analysis was performed using Kaplan-Meier and log-rank test.

Results: A total of 514 differentially expressed genes (DEGs) were identified between fluorouracil-resistant cell lines and fluorouracil-sensitive cell lines based on CCLE database. A total of 300 patients who had radical gastrectomy were recruited, of which 144 received fluorouracil-based chemotherapy and 156 were untreated. Three biomarkers (CTF1, BTN3A3, ADAD2) were finally selected by univariate and multivariate Cox regression analysis to establish the predictive models visualized by nomogram. This model could precisely predict both the Disease free survival (DFS) and Overall survival (OS) of patients treated with fluorouracil-based chemotherapy after surgery compared to untreated GC patients validated by both GEO database and TCGA database.

Conclusion: Our data established three genes-based predictive model which might predict GC patients' susceptibility to fluorouracil and help clinicians develop personalized treatment.

Keywords: Gastric cancer, Bioinformatics analysis, Cox regression analysis, Fluorouracil