Nomogram for predicting gastric cancer recurrence using biomarker gene expression

• Published in: European journal of surgical oncology 2020-01, Vol.46 (1), p.195-201
• Main Authors: Jeong, Sang-Ho, Kim, Rock Bum, Park, Sun Yi, Park, Jiho, Jung, Eun-Jung, Ju, Young-tae, Jeong, Chi-Young, Park, Miyeong, Ko, Gyung Hyuck, Song, Dae Hyun, Koh, Hyun Min, Kim, Woo-Ho, Yang, Han-Kwang, Lee, Young-Joon, Hong, Soon-Chan
• Format: Article
• Language: English
• Subjects: Biomarkers; Gastric neoplasm; Gene expression; Nomograms
• ISSN: 0748-7983; 1532-2157
• DOI: 10.1016/j.ejso.2019.09.143

Recently, researchers have tried to predict patient prognosis using biomarker expression in cancer patients. The aim of this study was to develop a nomogram predicting the 5-year recurrence-free probability (RFP) of gastric cancer patients using prognostic biomarker gene expression. We enrolled 360 patients in the training data set to develop the predictive model and nomogram. We analyzed the patients’ general variables and the gene expression levels of 10 prognostic biomarker candidates between the nonrecurrence and recurrence groups. We also performed external validation using 420 patients from the validation data set. The final nomogram was composed of age, sex, and the expression levels of CAPZA, PPase, OCT-1, PRDX4, gamma-enolase, and c-Myc. The five-year RFPs were 89%, 75%, 54% and 32% for the patients in the low-risk, intermediate-risk, high-risk and very-high-risk groups in the development cohort, respectively. In the external validation cohort, the 5-year RFPs were 89%, 75%, 63% and 60%, respectively. The areas under the curve were 0.718 (95% CI, 0.65–0.78) and 0.640 (95% CI, 0.57–0.70) for the training and validation data sets, respectively. The RFP Kaplan-Meier curves were significantly different among the 4 groups in the training and validation data sets (p