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

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 ti

SUBMITTER: Sumpio BJ 

PROVIDER: S-EPMC12491514 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature