Li Fei, Li Xinji, Niu Tianhui, Li Xiaoxin, Guan Ling, Wang Zhiyong, Liang Bin, Li Yuanyuan, Hao Zhiwei, Sui Chengyu
Department of Dermatology, Air Force Medical University, Air Force Medical Center, PLA, Beijing, China.
Department of Radiation Oncology, Air Force Medical University, Air Force Medical Center, PLA, Beijing, China.
Transl Cancer Res. 2025 Aug 31;14(8):5155-5165. doi: 10.21037/tcr-2025-954. Epub 2025 Jul 17.
Cutaneous melanoma (CM) exhibits considerable heterogeneity, and the immune status of patients can serve as a prognostic indicator. The increasing significance of immune-related markers in cancer prognosis provides clinicians with valuable tools for risk stratification and management decisions. The objective of this study was to develop a predictive model for assessing the risk of CM based on novel subtypes delineated according to immune-related genes.
This study included a cohort from The Cancer Genome Atlas (TCGA). Immune-related genes were carefully selected, and a comprehensive analysis was performed to characterize the molecular alterations and clinical implications linked to these genes. From this, an immune-related risk scoring system aimed at predicting the survival outcomes of patients diagnosed with CM was developed.
In this study, using an unsupervised consensus clustering algorithm, the study identified two subtypes-Cluster 1 (C1) and Cluster 2 (C2)-within the TCGA melanoma (MEL) cohort based on 1,959 immune-related genes. Survival analysis indicated that C1 was linked to poorer overall survival (OS) as compared to C2. We found significant correlations between these subtypes and clinical variables including tumor-node-metastasis (TNM) classification, new tumor events, and radiation therapy. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that 161 genes upregulated in C1 were associated with tyrosine metabolism, melanogenesis, and the p53 signaling pathway, while downregulated genes in C1 were linked to hematopoietic cell lineage, cytokine-cytokine receptor interactions, and cell adhesion molecules. Immune-related genes in CM were optimized and assessed using univariate Cox regression and a protein-protein interaction (PPI) network, with 20 genes being identified, including , , , , , , , , , , , , , , , , , , , and IFNG. From these, four key prognostic markers (CXCL10, IL10, B2M, and IFNG) were selected via a least absolute shrinkage and selection operator (LASSO) regression penalty approach and multivariate Cox analyses. For the prediction of the 1-, 3-, and 5-year survival rates, the immune-related risk score yielded area under the curve (AUC) values of 0.671, 0.667, and 0.676, respectively.
CM was divided into two subtypes based on immune gene expression, with the C1 subtype associated with poor prognosis. A prognostic risk model was developed using these classifications to predict patient outcomes.
皮肤黑色素瘤（CM）表现出显著的异质性，患者的免疫状态可作为预后指标。免疫相关标志物在癌症预后中的重要性日益增加，为临床医生提供了用于风险分层和管理决策的有价值工具。本研究的目的是基于根据免疫相关基因划分的新亚型，开发一种评估CM风险的预测模型。
本研究纳入了来自癌症基因组图谱（TCGA）的队列。仔细选择免疫相关基因，并进行全面分析以表征与这些基因相关的分子改变和临床意义。据此，开发了一种旨在预测CM诊断患者生存结果的免疫相关风险评分系统。
在本研究中，使用无监督一致性聚类算法，基于1959个免疫相关基因，在TCGA黑色素瘤（MEL）队列中鉴定出两个亚型——簇1（C1）和簇2（C2）。生存分析表明，与C2相比，C1与较差的总生存期（OS）相关。我们发现这些亚型与包括肿瘤-淋巴结-转移（TNM）分类、新肿瘤事件和放射治疗在内的临床变量之间存在显著相关性。京都基因与基因组百科全书（KEGG）通路分析显示，C1中上调的161个基因与酪氨酸代谢、黑色素生成和p53信号通路相关，而C1中下调的基因与造血细胞谱系、细胞因子-细胞因子受体相互作用和细胞粘附分子相关。使用单变量Cox回归和蛋白质-蛋白质相互作用（PPI）网络对CM中的免疫相关基因进行优化和评估，鉴定出20个基因，包括、、、、、、、、、、、、、、、、、、、和IFNG。从中，通过最小绝对收缩和选择算子（LASSO）回归惩罚方法和多变量Cox分析选择了四个关键预后标志物（CXCL10、IL10、B2M和IFNG）。对于1年、3年和5年生存率的预测，免疫相关风险评分的曲线下面积（AUC）值分别为0.671、0.667和0.676。
基于免疫基因表达将CM分为两个亚型，C1亚型预后较差。利用这些分类开发了一种预后风险模型来预测患者预后。
