Introduction:　The　majority　of　liver　cancer　cases　(90%)　are　attributed　to　hepatocellular　carcinoma　(HCC),　which　exhibits　significant　heterogeneity　and　an　unfavorable　prognosis.　Modulating　the　immune　response　and　metabolic　processes　play　a　crucial　role　in　both　the　prevention　and　treatment　of　HCC.　However,　there　is　still　a　lack　of　comprehensive　understanding　regarding　the　immune-related　metabolic　genes　that　can　accurately　reflect　the　prognosis　of　HCC.　Methods:　In　order　to　address　this　issue,　we　developed　a　prognostic　prediction　model　based　on　immune　and　metabolic　genes.　To　evaluate　the　accuracy　of　our　model,　we　performed　survival　analyses　including　Kaplan-Meier　(K-M)　curve　and　time-dependent　receiver　operating　characteristic　(ROC)　curve.　Furthermore,　we　compared　the　predictive　performance　of　our　risk　model　with　existing　models.　Finally,　we　validated　the　accuracy　of　our　risk　model　using　mouse　models　with　in　situ　transplanted　liver　cancer.　Results:　By　conducting　lasso　regression　analysis,　we　identified　four　independent　prognostic　genes:　fatty　acid　binding　protein　6　(FABP6),　phosphoribosyl　pyrophosphate　amidotransferase　(PPAT),　spermine　synthase　(SMS),　and　dihydrodiol　dehydrogenase　(DHDH).　Based　on　these　findings,　we　constructed　a　prognostic　model.　Survival　analysis　revealed　that　the　high-risk　group　had　significantly　lower　overall　survival　(OS)　rates.　Besides　that,　the　ROC　curve　demonstrated　the　effective　prognostic　capability　of　our　risk　model　for　hepatocellular　carcinoma　(HCC)　patients.　Furthermore,　through　animal　experiments,　we　validated　the　accuracy　of　our　model　by　showing　a　correlation　between　high-risk　scores　and　poor　prognosis　in　tumor　development.　Discussion:　In　conclusion,　our　prognostic　model　surpasses　those　solely　based　on　immune　genes　or　metabolic　genes　in　terms　of　accuracy.　We　observed　variations　in　prognosis　among　different　risk　groups,　accompanied　by　distinct　immune　and　metabolic　characteristics.　Therefore,　our　model　provides　an　original　evaluation　index　for　personalized　clinical　treatment　strategies　targeting　HCC　patients.　Copyright　©　2024　Zhuo,　Xia,　Lan,　Chen,　Wang　and　Liu.