FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction

Predicting spatial gene expression from routine H&E enables large-scale molecular profiling, yet current models treat this as isolated pointwise tasks, thereby overlooking essential biological structures like gene coordination and spatial distribution. To preserve these relationships, we introduce $\mathbf{\text{FLAG}}$, a diffusion-based framework that redefines this task as structured distribution modeling. At the same time, we identify the critical $\mathbf{\text{Gene}}\mathbf{\text{Dimension}}\mathbf{\text{Curse}}$, where joint modeling gene expression and their spatial interactions fail in high-dimensional spaces, and FLAG solves this challenge by integrating a spatial graph encoder for topological consistency and utilizing Gene Foundation Model (GFM) alignment for gene-gene fidelity in the generation process. To rigorously assess model performance, we propose a set of novel structural evaluation metrics, including Gene Structural Correlation ($\mathbf{\text{GSC}}$) and Spatial Structural Correlation ($\mathbf{\text{SSC}}$). Our experiments demonstrate that FLAG is highly competitive in traditional accuracy (PCC/MSE) while achieving significantly enhanced structural fidelity in capturing both gene-gene and gene-spatial relationships. The code is available at https://github.com/darkflash03/FLAG.