The scarcity of paired gene expression and pathology images datasets poses a major bottleneck for training large-scale pathology foundation models. Although gene-to-image generative models offer a promising solution, existing methods typically employ coarse-grained conditional control strategies, resulting in entanglement between background textures and cell layouts. To address this challenge, we propose GeneCaDiff, a three-stage cascaded diffusion model that explicitly aligns the generative process with the hierarchical organization of biological tissues. Two conditional DDPMs independently synthesize tissue backgrounds and cellular foreground components, and a subsequent ControlNet-based fusion generator utilizes niche and cellular community maps as dual conditions to synthesize realistic tissue images. Quantitative and qualitative evaluations demonstrate strong realism and diversity, while controllability experiments validate hierarchical, decoupled control over niche textures and community layouts.