Identification of Signature Genes and Characterizations of Tumor Immune Microenvironment and Tumor Purity in Lung Adenocarcinoma Based on Machine Learning.

ABSTRACT: The implication of the Estimation of Stromal and Immune cells in Malignant tumor tissues using expression data (ESTIMATE) method to determine the tumor microenvironment (TME) and tumor immune score including tumor purity represents an efficient method to identify and assess biomarkers for immunotherapy response in precision medicine. In this study we utilized a machine learning algorithm to analyze the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) lung adenocarcinoma (LUAD) transcriptome data to evaluate the association between TME and tumor purity. Furthermore, we investigated whether fewer TME components or a few dominant genes can infer tumor purity. The results indicated that the 29 immune infiltrating components determined by the ssGSEA method could screen the

SUBMITTER: Feng H 

PROVIDER: S-EPMC8916235 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature