Spatial transcriptomics provides valuable molecular maps, but current assays remain costly, sparse, and difficult to scale across large tissue regions. PH2ST uses limited spatial transcriptomics signals as prompts to guide multi-scale histological representation learning with a hypergraph framework for robust gene expression prediction from H&E slides. Across public datasets and realistic prompt settings, it outperforms prior methods and supports applications such as missing-spot imputation, super-resolution, and local-to-global prediction.