Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature - Sun, Zepang; Chen, Hao; Han, Zhen; Huang, Weicai; Hu, Yanfeng; Zhao, Mingli; Lin, Tian; Yu, Jiang; Liu, Hao; Jiang, Yuming; Li, Guoxin (Frontiers in Genetics, 2020)
Purpose: Gastric cancer is a product of multiple genes abnormal, including modification of genetic and epigenetic. This study aims to integrate various biomolecules (miRNAs, mRNA and DNA methylation) into a genome-wide network and develops a nomogram to predict overall survival of GC.329 cases with GC are screened from TCGA as a training cohort and a random 150 examples is validation cohort. A genome-wide network is constructed based on batch Cox regression and LASSO. Nomogram is established to predict 1, 3 and 5-year OS in training cohort, which is then assessed with calibration, discrimination and clinical usefulness in the validation cohort using R program. GO and KEGG analysis are performed to explain the function.Results: A genomics score with 7 miRNAs, 8 mRNA and 19 DNA methylation sites then divided into 9 models is a prognostic factor in multivariate analysis. Incorporating the genomics score into the genomics-based nomograms performs better in the estimation of OS than any single biomarker and the traditional TNM staging system, which could add prognostic value to the TMN staging. Multiple molecules and pathways are revealed to be related to the genome-wide network using DAVID 6.8.