Predicting the survival of patients with lung adenocarcinoma using a four-gene prognosis risk model.

Lung adenocarcinoma (LAD) is difficult to diagnose as it tends to be small in size and metastasize early. The aim of the present study was to investigate prognostic factors for patients with LAD and establish a prognosis risk model. A training set consisting of clinical and RNA sequencing data from 503 patients with LAD, as well as expression data from a further 59 LAD and adjacent tissues, was obtained from The Cancer Genome Atlas. Additionally, a validation dataset was acquired from the Gene Expression Omnibus database (GSE26939), which included clinical and gene expression data from 115 patients. Using the DESeq2 package to compare expression between LAD and adjacent tissues, differentially expressed genes (DEGs) were identified. On the basis of survival and the random forests for survival, regression and classification package, genes for constructing the prognosis risk model were selected. The prognosis risk model was constructed and validated using the survival package. Subsequently, high- and low-risk groups were compared using the Limma package to identify DEGs, and enrichment analysis was performed using the web-based gene set analysis toolkit. A protein-protein interaction network was visualized using Cytoscape software. There were 18,567 DEGs between the LAD samples and the adjacent tissues, and 363 DEGs between the high- and low-risk groups. Of these, four genes were selected for constructing the prognosis risk model, myosin IE (