Background.　Emerging　evidence　implicates　the　correlation　of　embryonic　germline　genes　with　the　tumor　progress　and　patient＇s　outcome.　However,　the　prognostic　value　of　these　genes　in　lung　adenocarcinoma　(LUAD)　has　not　been　fully　studied.　Here　we　systematically　evaluated　this　issue,　and　constructed　a　novel　signature　and　a　nomogram　associated　with　embryonic　germline　genes　for　predicting　the　outcomes　of　lung　adenocarcinoma.　Methods.　The　LUAD　cohorts　retrieved　from　The　Cancer　Genome　Atlas　(TCGA)　and　Gene　Expression　Omnibus　(GEO)　database　were　used　as　training　set　and　testing　set,　respectively.　The　embryonic　germline　genes　were　downloaded　from　the　website　https://venn.lodder.dev.　Then,　the　differentially　expressed　embryonic　germline　genes　(DEGGs)　between　the　tumor　and　normal　samples　were　identified　by　limma　package.　The　functional　enrichment　and　pathway　analyses　were　also　performed　by　clusterProfiler　package.　The　prognostic　model　was　constructed　by　the　least　absolute　shrinkage　and　selection　operator　(LASSO)-Cox　regression　method.　Survival　and　Receiver　Operating　Characteristic　(ROC)　analyses　were　performed　to　validate　the　model　using　training　set　and　four　testing　GEO　datasets.　Finally,　a　prognostic　nomogram　based　on　the　signature　genes　was　constructed　using　multivariate　regression　method.　Results.　Among　the　identified　269　DEGGs,　249　were　up-regulated　and　20　were　down　regulated.　GO　and　KEGG　analyses　revealed　that　these　DEGGs　were　mainly　enriched　in　the　process　of　cell　proliferation　and　DNA　damage　repair.　Then,　103　DEGGs　with　prognostic　value　were　identified　by　univariate　Cox　regression　and　further　filtered　by　LASSO　method.　The　resulting　sixteen　DEGGs　were　included　in　step　multivariate　Cox　regression　and　an　eleven　embryonic　germline　gene　related　signature　(EGRS)　was　constructed.　The　model　could　robustly　stratify　the　LUAD　patients　into　high-risk　and　low-risk　groups　in　both　training　and　testing　sets,　and　low-risk　patients　had　much　better　outcomes.　The　multi-ROC　analysis　also　showed　that　the　EGRS　model　had　the　best　predictive　efficacy　compared　with　other　common　clinicopathological　factors.　The　EGRS　model　also　showed　robust　predictive　ability　in　four　independent　external　datasets,　and　the　area　under　curve　(AUC)　was　0.726　(GSE30219),　0.764　(GSE50081),　0.657　(GSE37745)　and　0.668　(GSE72094).　More　importantly,　the　expression　level　of　some　genes　in　EGRS　has　a　significant　correlation　with　the　progression　of　LUAD　clinicopathology,　suggesting　these　genes　might　play　an　important　role　in　the　progression　of　LUAD.　Finally,　based　on　EGRS　genes,　we　built　and　calibrated　a　nomogram　for　conveniently　evaluating　patients＇　outcomes.