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

Yushan Zhang1, Hongliang Zeng2, Yibo Hu1

  • 1Department of Dermatology, The Third Xiangya Hospital, Central South University, Changsha, China.

Frontiers in Immunology
|November 17, 2022
PubMed
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Machine learning models can predict post-inflammatory skin pigmentation changes. Key cytokines like IL-37, CXCL13, CXCL1, CXCL2, and IL-19 are crucial in regulating these pigment abnormalities.

Area of Science:

  • Dermatology
  • Immunology
  • Computational Biology

Background:

  • Post-inflammatory hyperpigmentation and hypopigmentation are common skin conditions with unknown causes.
  • Predicting skin pigmentation outcomes after inflammation is challenging due to the lack of reliable methods.

Purpose of the Study:

  • To identify inflammatory genes involved in melanogenesis.
  • To develop machine learning models for predicting post-inflammatory skin pigmentation.
  • To investigate the role of specific cytokines in pigment cells.

Main Methods:

  • Analysis of 5 Gene Expression Omnibus (GEO) datasets to screen for inflammatory genes.
  • Development of machine learning models (LASSO, logistic regression, Random Forest) using candidate cytokines.
  • In vitro evaluation of cytokine effects on melanogenesis-related gene expression and tyrosinase activity in pigment cells.
Keywords:
IL-37evaluation modelsmachine learningmelanogenesispost-inflammatory pigmentation abnormalities

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Main Results:

  • IL-37, CXCL13, CXCL1, CXCL2, and IL-19 demonstrated high predictive value in machine learning models (AUC > 0.7).
  • IL-37 increased melanin content, melanogenesis-related gene expression (MITF, TYR, TYRP1, DCT), and tyrosinase activity.
  • CXCL13, CXCL1, CXCL2, and IL-19 were found to down-regulate melanogenesis-related gene expression.

Conclusions:

  • Machine learning models show promise in predicting post-inflammatory pigmentation abnormalities.
  • IL-37, CXCL1, CXCL2, CXCL13, and IL-19 are key regulators of post-inflammatory pigmentation.