Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes.

ABSTRACT: Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariat

SUBMITTER: Wu W 

PROVIDER: S-EPMC8806418 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature