Background　In　lung　adenocarcinoma　(LUAD),　there　remains　a　dearth　of　efficacious　diagnostic　studies　including　some　inflammation-related　genes　to　identify　the　LUAD　subgroups　with　different　clinical　outcomes.　Methods　First,　two　molecular　subgroups　were　identified　with　mRNA　expression　profiling　from　The　Cancer　Genome　Atlas　(TCGA)　by　K-means　algorithm.　Gene　set　enrichment　analysis　(GSEA),　immune　infiltration,　and　Gene　set　variation　analysis　(GSVA)　were　applied　to　explore　the　biological　functions　between　these　two　subtypes.　Then,　univariate　and　multivariate　Cox　regression　analyses　were　selected　to　evaluate　the　independence　of　these　subtypes　in　LUAD.　Next,　lasso　regression　was　applied　to　identify　the　high-precision　mRNAs　to　predict　the　subtype　with　favorable　prognosis.　Finally,　a　two-mRNA　model　was　constructed　using　the　method　of　multivariate　Cox　regression,　and　the　effectiveness　of　the　model　was　validated　in　a　training　set　(n　=　310)　and　three　independent　validation　sets　(n　=　1.　Results　Comprehensive　genomic　analysis　was　conducted　of　310　LUAD　samples　and　identified　two　subtypes　associated　with　molecular　classification　and　clinical　prognosis:　immune-enriched　and　non-immune-enriched　subgroup.　Then,　a　new　model　was　developed　based　on　two　mRNAs　(MS4A1　and　MS4A2)　in　TCGA　dataset　and　divided　these　LUAD　patients　into　high-risk　and　low-risk　subgroup　with　significantly　different　prognosis　(HR　=　1.644　(95%　CI　1.153-2.342);　p　＜　0.01),　which　was　independence　of　the　other　clinical　factors　(p　＜　0.05).　In　addition,　this　new　model　had　similar　predictive　effects　in　another　three　independent　validation　sets　(HR　＞　1.445,　p　＜　0.01).　Conclusions　We　constructed　a　robust　model　for　predicting　the　risk　of　LUAD　patients　and　evaluated　the　clinical　outcomes　independently　with　strong　predictive　power.　This　model　stands　as　a　reliable　guide　for　implementing　personalized　treatment　strategy.