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卵巢癌脂质代谢相关基因预后模型的构建及免疫浸润分析

王兴粉 邓玥 杨丽华

王兴粉, 邓玥, 杨丽华. 卵巢癌脂质代谢相关基因预后模型的构建及免疫浸润分析[J]. 昆明医科大学学报, 2024, 45(4): 17-25. doi: 10.12259/j.issn.2095-610X.S20240403
引用本文: 王兴粉, 邓玥, 杨丽华. 卵巢癌脂质代谢相关基因预后模型的构建及免疫浸润分析[J]. 昆明医科大学学报, 2024, 45(4): 17-25. doi: 10.12259/j.issn.2095-610X.S20240403
Xingfen WANG, Yue DENG, Lihua YANG. Construction of Lipid Metabolism-Related Gene Prognostic Model and Immunoinfiltration Analysis of Ovarian Cancer[J]. Journal of Kunming Medical University, 2024, 45(4): 17-25. doi: 10.12259/j.issn.2095-610X.S20240403
Citation: Xingfen WANG, Yue DENG, Lihua YANG. Construction of Lipid Metabolism-Related Gene Prognostic Model and Immunoinfiltration Analysis of Ovarian Cancer[J]. Journal of Kunming Medical University, 2024, 45(4): 17-25. doi: 10.12259/j.issn.2095-610X.S20240403

卵巢癌脂质代谢相关基因预后模型的构建及免疫浸润分析

doi: 10.12259/j.issn.2095-610X.S20240403
基金项目: 国家自然科学基金资助项目(82360579);云南省“万人计划”名医专项基金资助项目(YNWR-MY-2019-037);昆明医科大学卵巢癌临床及基础研究科技创新团队(CXDT202008);昆明医科大学第二附属医院对外合作基金资助项目(2022dwhz06);云南省科技厅-昆明医科大学应用基础研究联合专项基金资助项目(202401AY070001-053)
详细信息
    作者简介:

    王兴粉(1997~),女,云南曲靖人,在读硕士研究生,主要从事妇科肿瘤研究工作

    通讯作者:

    杨丽华,E-mail:lihuazhang33@sina.com

  • 中图分类号: R737.31

Construction of Lipid Metabolism-Related Gene Prognostic Model and Immunoinfiltration Analysis of Ovarian Cancer

  • 摘要:   目的  构建卵巢癌(ovarian carcer,OC)脂质代谢相关预后模型,探讨脂质代谢相关生物标志物及免疫细胞浸润程度在OC预后预测中的作用。  方法  下载TCGA数据库中OC样本转录组数据和临床数据,MSigDB数据库获取脂质代谢相关基因(LMRGs),caret包将样本按1∶1随机分为训练集与验证集,单因素Cox分析得到与OC预后显著相关的LMRGs,LASSO-Cox分析筛选模型基因以构建预后模型,Kaplan-Meier曲线及受试者工作特征(ROC)曲线评估预后模型效能,并进行TCGA内部验证。最后构建列线图并采用CIBERSORT算法进行免疫浸润分析。  结果  构建了1个8基因的OC预后模型,生存分析显示高风险组与低风险组预后差异有统计学意义(P < 0.05)。AUC提示该模型具有中等程度预测效能;多因素Cox分析显示LMrisk是OC患者的独立预后因素(P < 0.001);免疫浸润分析显示LMrisk与OC免疫相关。  结论  OC患者预后模型可作为1种新的独立预后评估手段,LMrisk可作为稳健的预后生物标志物,可能具有进一步临床应用的价值。
  • 图  1  OC中差异表达的LMRGs的鉴定

    A:正常卵巢组织和OC中DEGs火山图;B:OC DEGs与LMRGs的韦恩图;C:LMRGs在正常卵巢组织和OC中的表达热图。

    Figure  1.  Identification of differentially expressed LMRGs in ovarian cancer

    图  2  差异表达的LMRGs生物学功能分析

    A~B:LMRGs GO分析气泡图和圈图;C~D:LMRGs KEGG富集分析气泡图和圈图。

    Figure  2.  Biological function analysis of differentially expressed LMRGs

    图  3  在训练集中构建OC预后模型并评价

    A~B:采用LASSO-Cox分析9个预后相关的LMRGs;C:森林图显示8个LMRGs表达水平与OS的关系;D:训练集中高、低风险组OS的Kaplan-Meier分析;E:模型预测训练集的时间依赖性ROC曲线;F:OC患者生存状态、模型基因表达水平与LMrisk的关系。

    Figure  3.  The prognosis model of ovarian cancer was constructed and evaluated in the training set

    图  4  OC患者列线图预后模型的构建及评估

    A:LMrisk及临床病理特征独立预后分析;B:列线图预测OC患者的1、3及5 a OS预测模型;C:训练集OC患者1、3及5 a OS校准曲线;D:训练集OC患者1、3及5 a OS的DCA。

    Figure  4.  Construction and evaluation of nomogram prognostic model for patients with ovarian cancer

    图  5  OC预后模型的内部验证

    A:验证集高、低风险组OS的Kaplan-Meier分析;B:预后模型预测验证集的时间依赖性ROC曲线;C:验证集中OC患者生存状态、模型基因表达水平与LMrisk的关系。

    Figure  5.  Internal validation of prognostic models for ovarian cancer

    图  6  免疫细胞浸润分析

    A:肿瘤微环境差异分析小提琴图;B:免疫细胞与8个模型基因、LMrisk的相关性分析热图;C:巨噬细胞M0、巨噬细胞M1、被激活的肥大细胞、被激活的CD4记忆T细胞、CD8 T细胞和滤泡辅助性T细胞与LMrisk相关性分析散点图。

    Figure  6.  Analysis of immune cell infiltration

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