实用肿瘤学杂志 ›› 2025, Vol. 39 ›› Issue (2): 134-143.doi: 10.11904/j.issn.1002-3070.2025.02.009

• 临床研究 • 上一篇    下一篇

构建基于染色质重塑相关基因的皮肤黑色素瘤预后预测模型

李勇, 刘冰梅   

  1. 哈尔滨医科大学附属第四医院皮肤科(哈尔滨 150028)
  • 收稿日期:2024-08-26 修回日期:2025-02-17 出版日期:2025-04-28 发布日期:2025-05-06
  • 通讯作者: 刘冰梅,E-mail:liubm555@126.com
  • 作者简介:李勇,男,(1995—),硕士研究生,从事皮肤病与性病学、皮肤肿瘤的研究。
  • 基金资助:
    哈尔滨医科大学附属第四医院特别资助科研项目(编号:HYDSYTB202213)

Construction of a prognostic prediction model for skin cutaneous melanoma based on chromatin remodeling-related genes

LI Yong, LIU Bingmei   

  1. Department of Dermatology,The Fourth Affiliated Hospital of Harbin Medical University,Harbin 150001, China
  • Received:2024-08-26 Revised:2025-02-17 Online:2025-04-28 Published:2025-05-06

摘要: 目的 本研究旨在探究染色质重塑相关基因(chromatin remodeling-related genes,CRRGs)与皮肤黑色素瘤(skin cutaneous melanoma,SKCM)患者总生存期(overall survival,OS)之间的关联,并构建风险评分预后预测模型。方法 基于TCGA和GTEx数据库,获得皮肤黑色素瘤中差异表达的CRRGs,进一步通过蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络分析和单因素Cox回归分析以及LASSO回归分析筛选皮肤黑色素瘤预后相关基因,并构建风险评分预后模型。根据风险评分的中位数,将皮肤黑色素瘤患者分为高风险组和低风险组。通过单样本基因集富集分析(single sample gene set enrichment analysis,ssGSEA)算法评估高、低风险组间皮肤黑色素瘤患者的免疫细胞浸润情况。结果 PPI网络分析筛选到15个hub基因,单因素Cox回归和LASSO回归分析筛选到5个与皮肤黑色素瘤患者OS相关的CRRGs,分别为MMP2、MMP9、SPP1、TNFSF11和TIMP1。基于5个预后基因构建风险评分,多因素Cox回归分析表明风险评分是皮肤黑色素瘤患者的独立预后因素(P<0.05)。生存分析结果显示高风险组皮肤黑色素瘤患者的OS短于低风险组(P<0.05)。免疫细胞浸润分析结果表明高低风险组患者间有16种免疫细胞的浸润比例存在显著差异,其中活化B细胞、未成熟B细胞、效应和记忆性CD8+T细胞以及活化的CD8+T细胞等在高风险组中的占比显著降低(P<0.05)。而CD56bright自然杀伤细胞、CD56dim自然杀伤细胞、γδT细胞和未成熟的树突状细胞等在高风险组中的占比显著升高(P<0.05),药物敏感性分析表明高风险和低风险皮肤黑色素瘤患者对克唑替尼、厄洛替尼、吉非替尼和维诺瑞滨的敏感性存在显著差异(P<0.01)。结论 本研究构建了由5个CRRGs组成的皮肤黑色素瘤预后模型,进一步揭示了皮肤黑色素瘤不同风险组患者的免疫微环境和药物敏感性差异,为皮肤黑色素瘤患者的个性化治疗提供重要参考。

关键词: 皮肤黑色素瘤, 染色质重塑, 预后基因, 免疫细胞浸润

Abstract: Objective This study aimed to investigate the association between chromatin remodeling-related genes(CRRGs)and overall survival(OS)of patients with skin cutaneous melanoma(SKCM),and to construct a risk score prognostic prediction model. Methods Based on the TCGA and GTEx databases,the differentially expressed CRRGs in SKCM were obtained,and the prognosis-related genes of SKCM were further screened by protein-protein interaction(PPI)network analysis,univariate Cox regression analysis,and LASSO regression analysis.A risk score prognostic model was constructed based on the prognosis-related genes.According to the median of the risk score,SKCM patients were divided into the high-risk and low-risk groups.The single sample gene set enrichment analysis(ssGSEA)algorithm was used to evaluate the immune cell infiltration of SKCM patients between the high-and low-risk groups. Results A total of 15 hub genes were screened out through the PPI network analysis.Univariate Cox regression and LASSO regression analysis screened out 5 CRRGs associated with OS in SKCM patients,namely MMP2,MMP9,SPP1,TNFSF11,and TIMP1.A risk score was constructed based on the 5 prognostic genes,and multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for SKCM patients(P<0.05).Survival analysis showed that SKCM patients in the high-risk group was shorter than that in the low-risk group(P<0.05).The results of immune cell infiltration analysis showed that there were significant differences in the infiltration ratios of 16 immune cells between the high-and low-risk groups,among which the proportions of activated B cells,immature B cells,effector and memory CD8+T cells and activated CD8+T cells in the high-risk group were significantly reduced(P<0.05).The proportion of CD56bright natural killer cells,CD56dim natural killer cells,γδT cells and immature dendritic cells in the high-risk group were significantly increased(P<0.05).The drug sensitivity analysis showed that there were significant differences in the sensitivity of high-risk and low-risk SKCM patients to crizotinib,erlotinib,gefitinib and vinorelbine(P<0.01). Conclusions This study constructed a prognosis model of SKCM composed of 5 CRRGs,further revealed the differences in the immune microenvironment and drug sensitivity in patients with different risk groups of SKCM,and provided an important reference for personalized treatment of SKCM patients.

Key words: Skin cutaneous melanoma, Chromatin remodeling-related genes, Prognostic genes, Immune cell infiltration

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