Background
Keloids are a fibroproliferative disease characterized by unsatisfactory therapeutic effects and a high recurrence rate.
Conclusions
In summary, our findings show that the characteristics of circulating metabolic fingerprinting manifest phenotypic variation in keloid onset.
Methods
Untargeted metabolomic analysis was performed to compare the metabolic features of 15 keloid patients with those of paired healthy volunteers in the discovery cohort. The circulating metabolic signatures were selected using the least absolute shrinkage. Furthermore, the selection operators were quantified using multiple reaction monitoring-based target metabolite detection methods in the training and test cohorts.
Objective
This study aimed to investigate keloid-related circulating metabolic signatures.
Results
More than ten thousand metabolic features were consistently observed in all the plasma samples from the discovery cohort, and 30 significantly different metabolites were identified. Four differentially expressed metabolites including palmitoylcarnitine, sphingosine, phosphocholine, and phenylalanylisoleucine, were discovered to be related to keloid risk in the training and test cohorts. In addition, using linear and logistic regression models, the respective risk scores for keloids based on a 4-metabolite fingerprint classifier were established to distinguish keloids from healthy volunteers. Conclusions: In summary, our findings show that the characteristics of circulating metabolic fingerprinting manifest phenotypic variation in keloid onset.