Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China
Shaobo Zhang,
Hongxia Xu,
Wei Li,
Jiuwei Cui,
Qingchuan Zhao,
Zengqing Guo,
Junqiang Chen,
Qinghua Yao,
Suyi Li,
Ying He,
Qiuge Qiao,
Yongdong Feng,
Hanping Shi,
Chunhua Song
Affiliations
Shaobo Zhang
Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University
Hongxia Xu
Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University)
Wei Li
Cancer Center of the First Hospital of Jilin University
Jiuwei Cui
Cancer Center of the First Hospital of Jilin University
Qingchuan Zhao
Department of Digestive Diseases, Xijing Hospital, Fourth Military Medical University
Zengqing Guo
Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital
Junqiang Chen
Department of Gastrointestinal Surgery, First Affiliated Hospital of Guangxi Medical University
Qinghua Yao
Department of Integrated Traditional Chinese and Western Medicine, Zhejiang Cancer Hospital and Key Laboratory of Traditional Chinese Medicine Oncology, Zhejiang Cancer Hospital
Suyi Li
Department of Nutrition and Metabolism of Oncology, Affiliated Provincial Hospital of Anhui Medical University
Ying He
Department of Clinical Nutrition, Chongqing General Hospital
Qiuge Qiao
Department of General Surgery, Second Hospital (East Hospital), Hebei Medical University
Yongdong Feng
Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hanping Shi
Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University
Chunhua Song
Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University
Abstract Background Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. Methods Patients’ baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. Results A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets – “high-risk” and “low-risk” based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. Conclusion The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.