Accession Number : AD1047544

Title :   Computational Analysis Identifies Putative Prognostic Biomarkers of Pathological Scarring in Skin Wounds

Descriptive Note : Journal Article - Open Access


Personal Author(s) : Nagaraja,Sridevi ; Chen,Lin ; DiPietro,Luisa ; Reifman,Jaques ; Mitrophanov,Alexander

Full Text :

Report Date : 20 Feb 2018

Pagination or Media Count : 13

Abstract : Background: Pathological scarring in wounds is a prevalent clinical outcome with limited prognostic options. The objective of this study was to investigate whether cellular signaling proteins could be used as prognostic biomarkers of pathological scarring in traumatic skin wounds. Methods: We used our previously developed and validated computational model of injury-initiated wound healing to simulate the time courses for platelets, 6 cell types, and 21 proteins involved in the inflammatory and proliferative phases of wound healing. Next, we analysed thousands of simulated wound-healing scenarios to identify those that resulted in pathological (i.e., excessive) scarring. Then, we identified candidate proteins that were levated (or decreased) at the early stages of wound healing in those simulations and could therefore serve as predictive biomarkers of pathological scarring outcomes. Finally, we performed logistic regression analysis and calculated the area under the receiver operating characteristic curve to quantitatively assess the predictive accuracy of the model-identified putative biomarkers. Results: We identified three proteins (interleukin-10, tissue inhibitor of matrix metalloproteinase-1, and fibronectin) whose levels were elevated in pathological scars as early as 2 weeks post-wounding and could predict a pathological scarring outcome occurring 40 days after wounding with 80 accuracy. Conclusion: Our method for predicting putative prognostic wound-outcome biomarkers may serve as an effective means to guide the identification of proteins predictive of pathological scarring.

Descriptors :   biological markers , wound infections , skin (anatomy) , predictive modeling , COMPUTATIONAL MODELING , pathology , SCARS , trauma , scenarios

Subject Categories : Medicine and Medical Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE