Bioinformatics and functional analyses of key genes in smoking-associated lung adenocarcinoma.

Bioinformatics and functional analyses of key genes in smoking-associated lung adenocarcinoma. Oncol Lett. 2019 Oct;18(4):3613-3622 Authors: Zhou D, Sun Y, Jia Y, Liu D, Wang J, Chen X, Zhang Y, Ma X Abstract Smoking is one of the most important factors associated with the development of lung cancer. However, the signaling pathways and driver genes in smoking-associated lung adenocarcinoma remain unknown. The present study analyzed 433 samples of smoking-associated lung adenocarcinoma and 75 samples of non-smoking lung adenocarcinoma from the Cancer Genome Atlas database. Gene Ontology (GO) analysis was performed using the Database for Annotation, Visualization and Integrated Discovery and the ggplot2 R/Bioconductor package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the R packages RSQLite and org.Hs.eg.db. Multivariate Cox regression analysis was performed to screen factors associated with patient survival. Kaplan-Meier and receiver operating characteristic curves were used to analyze the potential clinical significance of the identified biomarkers as molecular prognostic markers for the five-year overall survival time. A total of 373 differentially expressed genes (DEGs; |log2-fold change|≥2.0 and P<0.01) were identified, of which 71 were downregulated and 302 were upregulated. These DEGs were associated with 28 significant GO functions and 11 significant KEGG pathways (false discovery ...
Source: Oncology Letters - Category: Cancer & Oncology Tags: Oncol Lett Source Type: research