Establishment and validation of psoriasis evaluation models

A Route for Investigating Psoriasis: From the Perspective of the Pathological Mechanisms and Therapeutic Strategies of Cancer

Psoriasis is an incurable skin disease that develops in about two-thirds of patients before the age of 40 and requires lifelong treatment; its pathological mechanisms have not been fully elucidated. The core pathological process of psoriasis is epidermal thickening caused by the excessive proliferation of epidermal keratinocytes, which is similar to the key feature of cancer; the malignant proliferation of cancer cells causes tumor enlargement, suggesting that there is a certain degree of commonality between psoriasis and cancer. This article reviews the pathological mechanisms that are common to psoriasis and cancer, including the interaction between cell proliferation and an abnormal immune microenvironment, metabolic reprogramming, and epigenetic reprogramming. In addition, there are common therapeutic agents and drug targets between psoriasis and cancer. Thus, psoriasis and cancer share a common pathological mechanisms-drug targets-therapeutic agents framework. On this basis, it is proposed that investigating psoriasis from a cancer perspective is beneficial to enriching the research strategies related to psoriasis.

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

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.