Spatial transcriptomics (ST) enables gene expression profiling while preserving tissue architecture, but its high cost and limited throughput constrain large-scale applications. This study evaluated the biological utility of VirtualST, a conditional diffusion model for inferring spatial gene expression from H&E images, in colon adenocarcinoma (COAD), primary invasive ductal carcinoma of the breast (IDC), and breast cancer lymph node metastasis (LYMPH_IDC). Twelve samples with paired H&E images and ground-truth ST data were evaluated using leave-one-sample-out cross-validation. Inference started from pure noise, with ground-truth expression used only for post hoc evaluation, and the final prediction for each sample was obtained by averaging ten sampling runs. The mean gene-wise Pearson correlation coefficients (PCCs) were 0.467, 0.439, and 0.496 for COAD, IDC, and LYMPH_IDC, respectively, with better reconstruction of tumor epithelial, secretory, and proliferative programs. Using ground-truth ST epithelial scores from the held-out samples as an independent molecular reference, the predicted and ground-truth scores achieved within-sample Pearson correlations of 0.79 and 0.66 in IDC and LYMPH_IDC, respectively, with corresponding hotspot Dice coefficients of 0.64 and 0.44. These results indicate that VirtualST can recover within-sample spatial epithelial signals but is not suitable for absolute quantification across tissues or direct pathological region detection.