〔Abstract〕 To establish a tumor prognostic risk assessment model related to target genes of the miR-34 family. Methods Target genes of the miR-34 family were screened，and the scores of miR-34 target genes were as- sessed in 16 tumor types. Univariate Cox regression analysis was used to identify the tumor type with the strongest correlation between miR-34 target gene scores and overall survival（OS）. Gene Ontology（GO）and Kyoto Encyclo- pedia of Genes and Genomes（KEGG）analyses were performed to elucidate the functional roles and signaling path- ways of miR-34 target genes. A prognostic risk model based on the miR-34 target genes was constructed using uni- variate Cox and LASSO regression analyses. Quantitative real-time PCR（qPCR）and dual-luciferase reporter as- says were conducted to validate whether the target genes bind to miR-34 and measure their RNA expression levels in the relevant tumors. Additionally，the risk score was integrated with other clinical indicators to develop a nomo- gram prediction model for patient survival. Results A total of 65 target genes of the miR-34 family were screened. The cancer type exhibiting stronger correlation between the target gene scores and OS was lung adenocarcinoma （P = 0. 003，HR= 5. 150）. Furthermore，miR-34 target genes were predominantly enriched in oxidative stress pathways and various tumor-related processes. Three genes，LDHA，GALNT7，and SATB2，were identified as core components of the prognostic analysis model for lung adenocarcinoma. Additionally，the constructed nomogram model demonstrated robust predictive performance. Conclusion The risk model and prognosis model of lung ad- enocarcinoma constructed based on the key target genes of miR-34 have good predictive performance.