Frontiers in Immunology (Oct 2022)

Establishment and validation of evaluation models for post-inflammatory pigmentation abnormalities

  • Yushan Zhang,
  • Hongliang Zeng,
  • Yibo Hu,
  • Ling Jiang,
  • Chuhan Fu,
  • Lan Zhang,
  • Fan Zhang,
  • Xiaolin Zhang,
  • Lu Zhu,
  • Jinhua Huang,
  • Jing Chen,
  • Qinghai Zeng

DOI
https://doi.org/10.3389/fimmu.2022.991594
Journal volume & issue
Vol. 13

Abstract

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Post-inflammatory skin hyper- or hypo-pigmentation is a common occurrence with unclear etiology. There is currently no reliable method to predict skin pigmentation outcomes after inflammation. In this study, we analyzed the 5 GEO datasets to screen for inflammatory-related genes involved in melanogenesis, and used candidate cytokines to establish different machine learning (LASSO regression, logistic regression and Random Forest) models to predict the pigmentation outcomes of post-inflammatory skin. Further, to further validate those models, we evaluated the role of these candidate cytokines in pigment cells. We found that IL-37, CXCL13, CXCL1, CXCL2 and IL-19 showed high predictive value in predictive models. All models accurately classified skin samples with different melanogenesis-related gene scores in the training and testing sets (AUC>0.7). Meanwhile, we mainly evaluated the effects of IL-37 in pigment cells, and found that it increased the melanin content and expression of melanogenesis-related genes (MITF, TYR, TYRP1 and DCT), also enhanced tyrosinase activity. In addition, CXCL13, CXCL1, CXCL2 and IL-19 could down-regulate the expression of several melanogenesis-related genes. In conclusion, evaluation models basing on machine learning may be valuable in predicting outcomes of post-inflammatory pigmentation abnormalities. IL-37, CXCL1, CXCL2, CXCL13 and IL-19 are involved in regulating post-inflammatory pigmentation abnormalities.

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