Construction of a 26‑feature gene support vector machine classifier for smoking and non‑smoking lung adenocarcinoma sample classification.

ABSTRACT: The present study aimed to identify the feature genes associated with smoking in lung adenocarcinoma (LAC) samples and explore the underlying mechanism. Three gene expression datasets of LAC samples were downloaded from the Gene Expression Omnibus database through pre‑set criteria and the expression data were processed using meta‑analysis. Differentially expressed genes (DEGs) between LAC samples of smokers and non‑smokers were identified using limma package in R. The classification accuracy of selected DEGs were visualized using hierarchical clustering analysis in R language. A protein‑protein interaction (PPI) network was constructed using gene interaction data from the Human Protein Reference Database for the DEGs. Betweenness centrality was calculated for each node in the network and g

SUBMITTER: Yang L 

PROVIDER: S-EPMC5783520 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature