Evidence map›Paper›PMID 42399485›Full record

ArticleFunctional & integrative genomics2026

Multi-omics integration identifies macrophage senescence driven by the RUNX1-P53 axis as a key mechanism in diabetic foot ulcer.

Jin Wu, Yupei Chen, Jie He, Jiahuan Mei, Hong Zhu, Depu Zhou

Abstract read
In one paragraph

Article in Functional & integrative genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jin WuDepartment of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
Yupei ChenDepartment of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
Jie HeDepartment of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
Jiahuan MeiHangzhou Linping District Hospital of Integrated Traditional Chinese and Western Medicine, Hangzhou, 311100, Zhejiang, China.
Hong ZhuDepartment of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China. zhuhong@wmu.edu.cn.
Depu ZhouDepartment of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China. zhoudepu@wmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic foot ulcer (DFU) is a severe complication of diabetes, characterized by chronic inflammation and impaired healing. Although cellular and immunosenescence are associated with chronic wounds, the specific cell types, regulatory mechanisms, and pathological consequences within the DFU microenvironment remain poorly defined. We conducted an integrative multi-omics analysis combining bulk and single-cell RNA sequencing data from human DFU and control tissues. The novel macrophage senescence score (MSS) was constructed by integrating aging-related genome-wide association studies signals with DFU-specific transcriptional signatures. Machine learning was used to identify key regulators. Functional validation was conducted in high glucose-stimulated human macrophages in vitro. Our single-cell atlas revealed macrophages as the most prominently senescent stromal cell population in DFUs. These senescent macrophages exhibited a distinct pro-inflammatory senescence-associated secretory phenotype (SASP) and engaged in aberrant intercellular communication, notably enhancing the C-X-C motif chemokine ligand 8 (CXCL8) - atypical chemokine receptor 1 (ACKR1) signaling axis to endothelial cells. We developed and validated a disease-relevant MSS, which demonstrated superior predictive value for DFU. Mechanistically, the transcription factor runt-related transcription factor 1 (RUNX1) was identified as the top feature. Functional assays established that RUNX1, upregulated under diabetic conditions, and is associated with promoted P53 acetylation. This study establishes macrophage senescence as a central pathological mechanism in DFU, propelled by the RUNX1-P53 acetylation axis. We define its role in disrupting the wound microenvironment through aberrant SASP and cell crosstalk. The findings provide a novel predictive tool MSS and a precise molecular target with implications for prognostication and therapy.

Indexed as

Cellular SenescenceCore Binding Factor Alpha 2 SubunitDiabetic FootMacrophagesTumor Suppressor Protein p53HumansMultiomicsSignal TransductionCore Binding Factor Alpha 2 SubunitRUNX1 protein, humanTP53 protein, humanTumor Suppressor Protein p53Cellular senescenceDiabetic foot ulcerMacrophage senescenceMulti-omics integrationRUNX1-P53 axis

Identifiers

PMID42399485
PMCPMC13331828

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.