Herein, we employed The Cancer Genome Atlas (TCGA) and Cancer Imaging Archive (TCIA) databases to construct a pathomics model that integrates H&E-stained whole slide images (WSIs) with machine learning techniques to evaluate the MMP9 expression level in GBM and explore its underlying molecular pathological characteristics. The gene discussed is MMP9; the disease is glioblastoma.