Construction of the metabolism-related models for predicting prognosis and infiltrating immune phenotype in lung squamous cell carcinoma.

ABSTRACT: Purpose: Cancers often display disorder metabolism, which closely related to the poor outcome of patients. We aimed to establish prognostic models using metabolism-associated genes, and identify the key factor involved in metabolism in lung squamous cell carcinoma (LUSC). Materials and Methods: R package 'TCGA biolinks' was used to download the mRNA sequencing data of LUSC from TCGA. The clusterProfiler package was performed to analyze biological pathways. The online tool GEPIA2 and cox regression method were applied to identify the two gene lists associated with metabolism and prognosis of LUSC. The lasso modeling was conducted to establish prognostic models. The quantiseq method was used to identify the cellular abundance of expression matrix in TCGA-LUSC dataset. Immunohis

SUBMITTER: Ma Z 

PROVIDER: S-EPMC10647197 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature