Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society · 2025
Differential Gene Expression in Healing and Non-Healing Diabetic Foot Ulcers and Discovery of Novel Ratiometric Biomarker to Predict Healing Outcome.
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Eager Jessica M, Cortes-Troncoso Juan, Kalan Lindsay, Campbell Amy, Rao Amit, Weingarten Michael S, Grice Elizabeth A, Oropallo Alisha, Margolis David J, Sacan Ahmet, Spiller Kara L
Аннотация
Diabetic foot ulcers (DFUs) are common and difficult to treat because the mechanisms behind unsuccessful responses to treatment are poorly understood. The goals of this study were to identify differences in healing and non-healing human DFUs using debrided tissue samples and to identify possible biomarkers of non-healing. First, DFU tissue samples collected over 12 weeks of treatment from 27 subjects (n = 12 healing and n = 15 non-healing) were analysed using a focused panel of 227 inflammation and wound healing-related human genes and 16S ribosomal RNA amplicon sequence to identify microbial species. Gene expression and correlation with microbial species differed between healing and non-healing DFUs. While no individual genes analysed at the initial time point could accurately predict healing outcome 12 weeks later, several 2-gene ratios were highly accurate. The ratio of C3AR1/CCL22 predicted healing outcome in the discovery cohort with an area under the receiver operator characteristic (ROC) curve (AUC) of 0.96. The AUC was 0.80 when tested on 74 unique samples collected at later time points from the discovery cohort, and the AUC was 0.69 when validated in a completely independent cohort of n = 51 subjects and using quantitative reverse transcription polymerase chain reaction (qRTPCR) as a more translational method of detection. The AUC increased to 0.75 when initial wound area was included. Overall, the results suggest that differences in inflammation contribute to differential healing outcomes in human chronic DFUs, and associated biomarkers may be used to predict healing outcome to guide treatment decisions.