Introduction　Colorectal　Cancer　(CRC)　remains　a　leading　cause　of　cancer-related　mortality,　characterized　by　substantial　interpatient　heterogeneity　and　limited　effective　prognostic　biomarkers.Methods　To　address　this　gap,　we　constructed　a　robust　prognostic　model　by　integrating　over　100　machine　learning　algorithms-such　as　LASSO,　CoxBoost,　and　StepCox-based　on　transcriptomic　and　clinical　data　from　The　Cancer　Genome　Atlas　(TCGA)　and　Gene　Expression　Omnibus　(GEO)　cohorts.Results　Plexin-A3　(PLXNA3)　emerged　as　a　top　risk　gene　within　the　ensemble　model,　which　achieved　strong　predictive　performance,　surpassing　conventional　clinical　indicators.　Multi-omics　validation　confirmed　PLXNA3＇s　prognostic　relevance.　Spatial　and　single-cell　transcriptomics　demonstrated　their　enrichment　in　malignant　epithelial　regions　and　negative　association　with　immune　cell　infiltration,　particularly　CD8+　T　cells　and　plasma　cells.　Transcription　factor　(TF)　and　microRNA　(miRNA)　correlation　analyses　revealed　potential　upstream　regulators　of　PLXNA3　linked　to　tumor　stemness　and　immune　suppression.　Functional　enrichment　indicated　its　association　with　cell　cycle,　DNA　damage　repair,　and　interferon　signaling　pathways.　Immunohistochemistry　(IHC)　confirmed　PLXNA3　overexpression　in　tumor　tissues　and　its　correlation　with　nodal　metastasis.　Moreover,　drug　sensitivity　profiling　and　Connectivity　Map　(CMap)　analysis　identified　potential　compounds,　including　imatinib,　MS-275　and　fasudil,　capable　of　reversing　PLXNA3-driven　transcriptional　programs.Discussion　This　study　identifies　PLXNA3　as　a　novel　immune-related　biomarker　in　colorectal　cancer　and　elucidates　its　multifaceted　role　in　tumor　progression,　immune　evasion,　and　therapeutic　resistance.　These　findings　provide　a　foundation　for　incorporating　PLXNA3　into　precision　oncology　frameworks　for　gastrointestinal　malignancies.