Novel insight on predicting prognosis of gastric cancer based on inflammation.

ABSTRACT:

Background

The tumor microenvironment (TME) and inflammation play vital roles in the development and progression of gastric cancer (GC). However, there are no inflammation-related models that can predict the prognosis and immunotherapy response of GC patients. We aimed to establish a prognostic model based on an inflammation-related gene (IRG) signature that can predict poor clinical outcomes in GC.

Methods

We searched IRGs in The Cancer Genome Atlas (TCGA) database and identified genes differentially expressed in GC. The model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis and validated using Gene Expression Omnibus (GEO) database. Receiver operating characteristic (ROC) curve, principal component analysi

SUBMITTER: Ni Z 

PROVIDER: S-EPMC9641120 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature