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

BackgroundMacrophage 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.MethodsWe constructed a macrophage polarization gene signature (MPG...

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Main Authors: Chengyuan Xu, Siqi Zhang, Bin Sun, Zicheng Yu, Hailong Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Immunology
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Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2025.1639303/full
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author Chengyuan Xu
Chengyuan Xu
Siqi Zhang
Bin Sun
Zicheng Yu
Hailong Liu
author_facet Chengyuan Xu
Chengyuan Xu
Siqi Zhang
Bin Sun
Zicheng Yu
Hailong Liu
author_sort Chengyuan Xu
collection DOAJ
description BackgroundMacrophage 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.MethodsWe 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.ResultsThe 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.ConclusionsThis 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.
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spelling doaj-art-b5d5f272d2744dcbb03de8993e0700de2025-08-20T03:41:46ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-08-011610.3389/fimmu.2025.16393031639303Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosisChengyuan Xu0Chengyuan Xu1Siqi Zhang2Bin Sun3Zicheng Yu4Hailong Liu5Department of General, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, ChinaCenter for Clinical Research and Translational Medicine, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Pharmacy, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, ChinaCenter for Clinical Research and Translational Medicine, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Pharmacy, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of General, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, ChinaBackgroundMacrophage 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.MethodsWe 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.ResultsThe 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.ConclusionsThis 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.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1639303/fullrectal cancermacrophage polarizationPYGMmetabolismprognosismachine learning
spellingShingle Chengyuan Xu
Chengyuan Xu
Siqi Zhang
Bin Sun
Zicheng Yu
Hailong Liu
Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis
Frontiers in Immunology
rectal cancer
macrophage polarization
PYGM
metabolism
prognosis
machine learning
title Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis
title_full Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis
title_fullStr Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis
title_full_unstemmed Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis
title_short Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis
title_sort machine learning identifies pygm as a macrophage polarization linked metabolic biomarker in rectal cancer prognosis
topic rectal cancer
macrophage polarization
PYGM
metabolism
prognosis
machine learning
url https://www.frontiersin.org/articles/10.3389/fimmu.2025.1639303/full
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