Spatial　transcriptomics　reveals　the　spatial　distribution　of　genes　in　complex　tissues,　providing　crucial　insights　into　biological　processes,　disease　mechanisms,　and　drug　development.　The　prediction　of　gene　expression　based　on　cost-effective　histology　images　is　a　promising　yet　challenging　field　of　research.　Existing　methods　for　gene　prediction　from　histology　images　exhibit　two　major　limitations.　First,　they　ignore　the　intricate　relationship　between　cell　morphological　information　and　gene　expression.　Second,　these　methods　do　not　fully　utilize　the　different　latent　stages　of　features　extracted　from　the　images.　To　address　these　limitations,　we　propose　a　novel　hypergraph　neural　network　model,　HGGEP,　to　predict　gene　expressions　from　histology　images.　HGGEP　includes　a　gradient　enhancement　module　to　enhance　the　model＇s　perception　of　cell　morphological　information.　A　lightweight　backbone　network　extracts　multiple　latent　stage　features　from　the　image,　followed　by　attention　mechanisms　to　refine　the　representation　of　features　at　each　latent　stage　and　capture　their　relations　with　nearby　features.　To　explore　higher-order　associations　among　multiple　latent　stage　features,　we　stack　them　and　feed　into　the　hypergraph　to　establish　associations　among　features　at　different　scales.　Experimental　results　on　multiple　datasets　from　disease　samples　including　cancers　and　tumor　disease,　demonstrate　the　superior　performance　of　our　HGGEP　model　than　existing　methods.