Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis

Background Macrophage polarization plays a pivotal role in shaping the tumor microenvironment and influencing rectal cancer progression. However, the metabolic and prognostic regulators governing this process remain largely undefined. Methods We constructed a macrophage polarization gene signature (MPGS) by integrating weighted gene co-expression network analysis (WGCNA) with multiple machine learning algorithms across two independent cohorts: 363 rectal cancer samples from GSE87211 and 177 samples from The Cancer Genome Atlas (TCGA). The prognostic performance of MPGS was evaluated across rectal and multiple other cancer types. Functional analyses, single-cell RNA sequencing, immunohistochemistry of clinical specimens, and in vitro cellular assays were employed to investigate the role of the MPGS hub gene, PYGM , in tumor biology and immune modulation. Results The MPGS exhibited robust prognostic capability and effectively predicted responses to immunotherapy and various chemotherapeutic agents. Both MPGS and its central metabolic component, PYGM , were closely linked to M2 macrophage infiltration, immunosuppressive tumor microenvironments, and poor clinical outcomes in rectal adenocarcinoma. Single-cell transcriptomic analysis revealed that malignant epithelial cells with elevated PYGM expression are metabolically active and closely interact with M2 macrophages. Clinical tissue analyses and functional assays confirmed that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization. Conclusions This study firstly highlights PYGM as a key metabolic and immunological regulator in rectal cancer, with significant prognostic and therapeutic implications. MPGS and PYGM may serve as novel biomarkers for risk stratification and guide personalized treatment strategies in patients with rectal adenocarcinoma.