Background　Hepatocellular　carcinoma　(HCC)　is　highly　aggressive,　with　delayed　diagnosis,　poor　prognosis,　and　a　lack　of　comprehensive　and　accurate　prognostic　models　to　assist　clinicians.　This　study　aimed　to　construct　an　HCC　prognosis-related　gene　signature　(HPRGS)　and　explore　its　clinical　application　value.Methods　TCGA-LIHC　cohort　was　used　for　training,　and　the　LIRI-JP　cohort　and　HCC　cDNA　microarray　were　used　for　validation.　Machine　learning　algorithms　constructed　a　prognostic　gene　label　for　HCC.　Kaplan-Meier　(K-M),　ROC　curve,　multiple　analyses,　algorithms,　and　online　databases　were　used　to　analyze　differences　between　high-　and　low-risk　populations.　A　nomogram　was　constructed　to　facilitate　clinical　application.Results　We　identified　119　differential　genes　based　on　transcriptome　sequencing　data　from　five　independent　HCC　cohorts,　and　53　of　these　genes　were　associated　with　overall　survival　(OS).　Using　101　machine　learning　algorithms,　the　10　most　prognostic　genes　were　selected.　We　constructed　an　HCC　HPRGS　with　four　genes　(SOCS2,　LCAT,　ECT2,　and　TMEM106C).　Good　predictive　performance　of　the　HPRGS　was　confirmed　by　ROC,　C-index,　and　K-M　curves.　Mutation　analysis　showed　significant　differences　between　the　low-　and　high-risk　patients.　The　low-risk　group　had　a　higher　response　to　transcatheter　arterial　chemoembolization　(TACE)　and　immunotherapy.　Treatment　response　of　high-　and　low-risk　groups　to　small-molecule　drugs　was　predicted.　Linifanib　was　a　potential　drug　for　high-risk　populations.　Multivariate　analysis　confirmed　that　HPRGS　were　independent　prognostic　factors　in　TCGA-LIHC.　A　nomogram　provided　a　clinical　practice　reference.Conclusion　We　constructed　an　HPRGS　for　HCC,　which　can　accurately　predict　OS　and　guide　the　treatment　decisions　for　patients　with　HCC.