Constructing a new prognostic signature of gastric cancer based on multiple data sets.

ABSTRACT: In order to explore new prediction methods and key genes for gastric cancer. Firstly, we downloaded the 6 original sequencing data of gastric cancer on the Illumina HumanHT-12 platform from Array Expression and Gene Expression Omnibus, and used bioinformatics methods to identify 109 up-regulated genes and 271 down-regulated genes. Further, we performed univariate Cox regression analysis of prognostic-related genes, then used Lasso regression to remove collinearity, and finally used multivariate Cox regression to analyze independent prognostic genes (MT1M, AKR1C2, HEYL, KLK11, EEF1A2, MMP7, THBS1, KRT17, RPESP, CMTM4, UGT2B17, CGNL1, TNFRSF17, REG1A). Based on these, we constructed a prognostic risk proportion signature, and found that patients with high-risk gastric cancer have a high degr

SUBMITTER: Zhou L 

PROVIDER: S-EPMC8806649 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature