Evidence map›Paper›PMID 41038874›Full record

ArticleScientific reports2025

Integrated machine learning analysis of proteomic and transcriptomic data identifies healing associated targets in diabetic wound repair.

Brandon J Sumpio, Kyongmin Yeo, Georgios Theocharidis, Ikram Mezghani, Achille Fokoue, Alexander Gray, Jessica Gilman, Nikolaos Kalavros, Ioannis Vlachos, Samuel Sia and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Time to Heal: MicroRNA Temporal Dysregulation in Diabetic Foot Ulcers.International journal of molecular sciences · 2026
    Review
  2. The Wound-Heart Axis: Can Chronic Wounds Contribute to Cardiac Dysfunction?International journal of molecular sciences · 2026
    Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Brandon J Sumpio *Joslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Kyongmin Yeo *IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
Georgios Theocharidis *Joslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. gtheocha@bidmc.harvard.edu.
Ikram MezghaniJoslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Achille FokoueIBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
Alexander GrayCentaur AI Institute, Lincoln, CA, USA.
Jessica GilmanJoslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Nikolaos KalavrosDepartment of Pathology, HMS Initiative for RNA Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Cancer Research Institute, Boston, MA, USA.
Ioannis VlachosDepartment of Pathology, HMS Initiative for RNA Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Cancer Research Institute, Boston, MA, USA.
Samuel SiaDepartment of Engineering, Columbia University, New York, NY, USA.
Tengfei Ma *Department of Biomedical Informatics, Stony Brook University, New York, NY, USA. tengfei.ma@stonybrook.edu.
Aristidis Veves *Joslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. aveves@bidmc.harvard.edu.

Funding

Defense Advanced Research Projects Agency D20AC00004
6 · The paper itself

Abstract

This study identified biomarkers that could be leveraged to classify the state of healing in diabetic wounds. Firstly, by collecting wound samples from diabetic mice at different time points and generating their protein profiles using standard techniques, we set to interrogate whether a small number of biomarkers could serve as sensors to monitor the healing stage. Least absolute shrinkage and selection operator (LASSO) was applied. Large-scale analysis of wound tissue proteins integrated with the respective wound sizes allowed to establish a correlation between the observed protein profile and wound closure. We further evaluated human subjects' systemic serum proteomics for biomarkers. An additional wound healing model in diabetic mice was employed for microRNA quantitation at the same time points and similarly analyzed. Our analysis highlighted markers MMP-2, HGF, miR-1b and miR-107-3p in mice and Fractalkine and FGF-2 in humans that could correctly identify the extent of healing. By using proteomics from mice and human patients and complementary microRNA mouse data with computer regression models we can better predict molecular and protein deficits associated with impaired diabetic wound repair.

Indexed as

Diabetic FootGene Expression ProfilingMachine LearningProteomicsWound HealingAnimalsBiomarkersBlood GlucoseChemokine CX3CL1FemaleFibroblast Growth Factor 2Hepatocyte Growth FactorHumansLogistic ModelsMaleMatrix Metalloproteinase 2BiomarkersBlood GlucoseChemokine CX3CL1Fibroblast Growth Factor 2Hepatocyte Growth FactorMatrix Metalloproteinase 2MicroRNAs

Identifiers

PMID41038874
PMCPMC12491514

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Registered trials

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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.