Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases.

ABSTRACT: Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases ha

SUBMITTER: Martinez BA 

PROVIDER: S-EPMC9054015 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature