Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis.

ABSTRACT:

Background

Non-small cell lung cancer (NSCLC) is a common malignancy with a high morbidity and mortality rate worldwide, but the driver genes and signaling pathways involved are largely unclear. Herein, our study aimed to identify significant genes with poor outcome and underlying mechanisms in NSCLC using bioinformatics analyses.

Methods

Gene expression profiles (GSE33532, GSE19188, GSE102287, GSE27262), including 319 NSCLC and 232 adjacent lung tissues, were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by the GEO2R online tool. Functional and pathway enrichment analyses were performed via the DAVID database. The protein-protein interactions (PPIs) of these DEGs were constructed by the STRING website and visualized by the Cytoscape

SUBMITTER: Cui S 

PROVIDER: S-EPMC9091047 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature