Department of Cancer Center, The Second Hospital of Shandong University, Jinan, Shandong 250033, China.
Comput Math Methods Med. 2022 Aug 25;2022:4261329. doi: 10.1155/2022/4261329. eCollection 2022.
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.
皮肤黑色素瘤（SKCM）是一种常见的恶性皮肤癌。早期诊断可以在很大程度上有效降低 SKCM 患者的死亡率。我们成功构建了一个模型来检验 SKCM 患者的预后。从 TCGA 数据库下载了癌症基因图谱（TCGA-SKCM）的甲基化相关数据和临床数据。对甲基化数据进行预处理后，使用单因素 Cox 回归分析和多因素 Cox 回归分析获得了 21861 个与预后相关的潜在预后相关的甲基化位点。然后，使用无监督聚类将患者分为 4 个聚类，应用加权相关网络分析（WGCNA）构建共表达模块。通过重叠聚类和 turquoise 模型中的 CpG 位点，通过 LASSO Cox 回归和多因素 Cox 回归建立了预后模型。结果发现 9 个甲基化位点包括 cg01447831、cg14845689、cg20895058、cg06506470、cg09558315、cg06373660、cg17737409、cg21577036 和 cg22337438。构建预后模型后，通过生存分析和接受者操作特征（ROC）曲线验证模型的性能，并通过单因素和多因素回归验证模型的独立性。结果表明，该模型具有良好的预测能力，风险评分可作为 SKCM 患者的独立预后因素。最后，我们结合临床数据和患者的风险评分建立并验证了能够确定患者预后的列线图。结果发现，该列线图的可靠性相对较好。总之，我们通过分析 TCGA-SKCM 数据集的数据构建了一个能够确定 SKCM 患者预后的预后模型，并筛选出 9 个关键的甲基化位点。最后，结合临床诊断信息和风险评分建立了预后列线图。这些结果对提高 SKCM 患者的预后具有重要意义。
