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

Abstract

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.

Figure 1

Overall flowchart of this study.

Figure 2

Unsupervised clustering of SKCM patients. (a) Cumulative distribution function curve of unsupervised clustering. (b) Relative change in area under cumulative distribution function (CDF) curve. (c) Clustering heatmap of 4 types of SKCM patients. Each cluster represents a subgroup of patients.

Figure 3

Heatmap of top 5000 variance CpG sites, with red representing high level of DNA methylation and blue representing low level of DNA methylation.

Figure 4

Survival curve of SKCM patients' OS, with each line representing a subgroup of SKCM patients.

Figure 5

WGCNA analysis of the top 5000 variance CpG sites. (a) Analysis of the scale-free fit index for various soft-thresholding powers (β). (b) Analysis of the mean connectivity for various soft-thresholding powers. (c) Dendrogram of prognosis-related CpG sites clustered based on a dissimilarity measure (1-TOM). (d) Heatmap of the correlation between module and subgroups of patients.

Figure 6

Filtering of OS-associated CpG sites. (a) Scatter plot of turquoise module member CpG sites related to cluster 1. Each dot represents a CpG site. (b) Scatter plot of turquoise module member CpG sites related to cluster 2. (c) Venn plot of important CpG sites. The blue circle represents cluster 1-associated CpG sites, and the yellow circle represents cluster 2-associated CpG sites.

Figure 7

Construction of SKCM risk model. (a) LASSO coefficient profiles of key CpG sites. (b) Selection of the optimal parameter (lambda) in the LASSO model for TCGA-LUAD. (c) Key CpG sites filtered by multivariate Cox regression analysis. (d) Riskscore and survival status of patients in training cohort. (e) Heatmap of each CpG site in risk model.

Figure 8

Validation of SKCM risk model. (a and b) Survival curve analysis of training cohort and validation cohort. (c and d) ROC curve analysis of training cohort and validation cohort.

Figure 9

Univariate and multivariate validation of risk model's independence. (a) Univariate analysis validates independence of SKCM risk model. (b) Multivariate analysis validates independence of SKCM risk model.

Figure 10

Construction and validation of prognostic nomogram. (a) Nomogram to predict 1-, 3-, and 5-year survival rate of SKCM patients. (b–d) Fitting curves used to validate prognostic nomogram.