693 p38 signaling regulates human cutaneous metastatic melanoma (MM) invasion and MM-dependent disruption of keratinocyte differentiation

Kiss, Alexi; Wei, Calvin; Aligabi, Zahra; Barlas, Meral; Murphy, Eric A.; Glass, Frank; Friedman, Adam; Efimova, Tatiana

doi:10.1016/j.jid.2020.03.705

Cited by 1 publication

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“…Moreover, environmental factors are also important external factors for SKCM. For example, di-(2-ethylhexyl) phthalate (DEHP) in cosmetics and PM2.5 in the air can increase the incidence rate of SKCM [ 6 , 7 ]. Besides common environmental carcinogenic factors, genetic and epigenetic modifications are also common carcinogenic factors, and studies considered that people from families with CDKN2A gene mutation have higher incidence rate of melanoma [ 8 , 9 ].…”

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confidence: 99%

Construction of Prognostic Risk Model of Patients with Skin Cutaneous Melanoma Based on TCGA-SKCM Methylation Cohort

Cong

Wei

et al. 2022

Computational and Mathematical Methods in Medicine

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Skin cutaneous melanoma (SKCM) is a common malignant skin cancer. Early diagnosis could effectively reduce SKCM patient’s mortality to a large extent. We managed to construct a model to examine the prognosis of SKCM patients. The methylation-related data and clinical data of The Cancer Gene Atlas- (TCGA-) SKCM were downloaded from TCGA database. After preprocessing the methylation data, 21,861 prognosis-related methylated sites potentially associated with prognosis were obtained using the univariate Cox regression analysis and multivariate Cox regression analysis. Afterward, unsupervised clustering was used to divide the patients into 4 clusters, and weighted correlation network analysis (WGCNA) was applied to construct coexpression modules. By overlapping the CpG sites between the clusters and turquoise model, a prognostic model was established by LASSO Cox regression and multivariate Cox regression. It was found that 9 methylated sites included cg01447831, cg14845689, cg20895058, cg06506470, cg09558315, cg06373660, cg17737409, cg21577036, and cg22337438. After constructing the prognostic model, the performance of the model was validated by survival analysis and receiver operating characteristic (ROC) curve, and the independence of the model was verified by univariate and multivariate regression. It was represented that the prognostic model was reliable, and riskscore could be used as an independent prognostic factor in SKCM patients. At last, we combined clinical data and patient’s riskscore to establish and testify the nomogram that could determine patient’s prognosis. The results found that the reliability of the nomogram was relatively good. All in all, we constructed a prognostic model that could determine the prognosis of SKCM patients and screened 9 key methylated sites through analyzing data in TCGA-SKCM dataset. Finally, a prognostic nomogram was established combined with clinical diagnosed information and riskscore. The results are significant for improving the prognosis of SKCM patients in the future.

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