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.　GSE328613　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　signattues　(anln,　cenpa,　plk　1　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,　LEAD　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.