Identification of significant genes in non-small cell lung cancer by bioinformatics analyses.

ABSTRACT:

Background

Lung cancer is the most malignant cancer featured with undesirable prognosis. It is urgent to identify novel biomarkers to improve both diagnosis and prognosis. The purpose of the study was to identify significant genes involved in lung cancer through bioinformatic methods and reveal potential underlying mechanisms.

Methods

Three datasets GSE19188, GSE27262, GSE118375, containing 122 lung cancer and 96 normal tissues, were available from GEO database. GEO2R and Venn diagram online software were applied to pick out differentially expressed genes (DEGs). Next, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID) to analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO) enrichment, followed by protein-protei

SUBMITTER: Ye X 

PROVIDER: S-EPMC8799091 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature