Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer.

ABSTRACT:

Background

Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80-85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).

Methods

NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score mode

SUBMITTER: Ding H 

PROVIDER: S-EPMC9440521 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature