A transcriptomic study for identifying cardia‐ and non–cardia‐specific gastric cancer prognostic factors using genetic algorithm‐based methods

• Published in: Journal of cellular and molecular medicine Vol. 24; no. 16; pp. 9457 - 9465
• Main Authors: Xin, Junyi, Wu, Yanling, Wang, Xiaowei, Li, Shuwei, Chu, Haiyan, Wang, Meilin, Du, Mulong, Zhang, Zhengdong
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.08.2020; John Wiley and Sons Inc
• Subjects: Algorithms; biomarker; Biomarkers; Biomarkers, Tumor - genetics; Cardia - metabolism; Cardia - pathology; Case-Control Studies; Computational Biology; Datasets; Epidemiology; Feature selection; Female; Follow-Up Studies; Gastric cancer; Gene expression; Gene Expression Regulation, Neoplastic; genetic algorithm; Genetic algorithms; Genomes; Humans; Male; Medical prognosis; Methods; Mutation; Nomograms; Original; Prognosis; Regression analysis; ROC Curve; site specificity; Stomach Neoplasms - genetics; Stomach Neoplasms - pathology; Studies; Support vector machines; Survival; Survival analysis; Survival Rate; Transcriptome; Tumors; Variables; Austria; genetic algorithm; gastric cancer; biomarker; site specificity; prognosis
• ISSN: 1582-1838; 1582-4934; 1582-4934
• DOI: 10.1111/jcmm.15618

Gastric cancer (GC) is a heterogeneous tumour with numerous differences of epidemiologic and clinicopathologic features between cardia cancer and non‐cardia cancer. However, few studies were performed to construct site‐specific GC prognostic models. In this study, we identified site‐specific GC transcriptomic prognostic biomarkers using genetic algorithm (GA)‐based support vector machine (GA‐SVM) and GA‐based Cox regression method (GA‐Cox) in the Cancer Genome Atlas (TCGA) database. The area under time‐dependent receive operating characteristic (ROC) curve (AUC) regarding 5‐year survival and concordance index (C‐index) was used to evaluate the predictive ability of Cox regression models. Finally, we identified 10 and 13 prognostic biomarkers for cardia cancer and non‐cardia cancer, respectively. Compared to traditional models, the addition of these site‐specific biomarkers could notably improve the model preference (cardia: AUCtraditional vs AUCcombined = 0.720 vs 0.899, P = 8.75E‐08; non‐cardia: AUCtraditional vs AUCcombined = 0.798 vs 0.994, P = 7.11E‐16). The combined nomograms exhibited superior performance in cardia and non‐cardia GC survival prediction (C‐indexcardia = 0.816; C‐indexnoncardia = 0.812). We also constructed a user‐friendly GC site‐specific molecular system (GC‐SMS, https://njmu‐zhanglab.shinyapps.io/gc_sms/), which is freely available for users. In conclusion, we developed site‐specific GC prognostic models for predicting cardia cancer and non‐cardia cancer survival, providing more support for the individualized therapy of GC patients.