Objective Lung cancer is one of the most common cancers in the world. Lung adenocarcinoma (LUAD) has the highest annual mortality rate among lung cancer patients. It has been reported that changes in gene spectrum were associated with the process of tumorigenesis and its development. The purpose of this study is to identify the gene signatures associated with LUAD and to further analyze their prognostic significance.Methods Weighted gene co-expression network analysis (WGCNA), differential gene analysis, cox regression analysis, and protein-protein interaction (PPI) network analysis were used to screen the hub genes highly related to LUAD based on The Cancer Genome Atlas (TCGA) database. The RNA-seq data sets from TCGA and GTEx (Genotype Tissue Expression) database were combined and divided into a training set and a validation set, which were used to construct the diagnostic model by support vector machine recursive feature elimination feature (SVM-RFE) algorithm. GSE32863 and GSE31210 were used to verify the diagnostic accuracy of the model and the prognostic value of our obtained gene signatures, respectively.Results The results demonstrated that the model of 5 gene signatures (anln, cenpa, plk1, tpx2, cdca3) obtained by the SVM-RFE algorithm had an outstanding performance in the classification of LUAD patients. Functional enrichment analysis showed that these 5 gene signatures were highly related to the biological process of tumor initiation and progression. What’s more, LUAD patients with high expression of these 5 genes also exerted a poor outcome in survival status.Conclusion Therefore, we could conclude that our study obtained useful models with 5 gene signatures for the diagnosis and prognosis of LUAD, which were essential for the development of novel targets applied in precision therapy.