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

Junyi Xin; Yanling Wu; Xiaowei Wang; Shuwei Li; Haiyan Chu; Meilin Wang; Mulong Du; Zhengdong Zhang

doi:10.1111/jcmm.15618

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

J Cell Mol Med. 2020 Aug;24(16):9457-9465. doi: 10.1111/jcmm.15618. Epub 2020 Jul 10.

Authors

Junyi Xin^{1

2}, Yanling Wu^{1

2}, Xiaowei Wang^{1

2}, Shuwei Li^{1

2}, Haiyan Chu^{1

2}, Meilin Wang^{1

2}, Mulong Du³, Zhengdong Zhang^{1

2}

Affiliations

¹ Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
² Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
³ Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.

Abstract

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: AUC_traditional vs AUC_combined = 0.720 vs 0.899, P = 8.75E-08; non-cardia: AUC_traditional vs AUC_combined = 0.798 vs 0.994, P = 7.11E-16). The combined nomograms exhibited superior performance in cardia and non-cardia GC survival prediction (C-index_cardia = 0.816; C-index_noncardia = 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.

Keywords: biomarker; gastric cancer; genetic algorithm; prognosis; site specificity.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Biomarkers, Tumor / genetics*
Cardia / metabolism
Cardia / pathology*
Case-Control Studies
Computational Biology
Female
Follow-Up Studies
Gene Expression Regulation, Neoplastic*
Humans
Male
Nomograms*
Prognosis
ROC Curve
Stomach Neoplasms / genetics
Stomach Neoplasms / pathology*
Survival Rate
Transcriptome*

Substances

Biomarkers, Tumor