Construction of Lipid Metabolism-Related Gene Prognostic Model and Immunoinfiltration Analysis of Ovarian Cancer
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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。 Abstract:Objective To construct a lipid metabolism-related prognostic model for ovarian cancer （OC）, and to investigate the role of lipid metabolism-related biomarkers and the degree of immune cell infiltration in prognosis prediction of OC. Methods Transcriptional data and clinical data of OC samples were downloaded from the TCGA database, and lipid metabolism-related genes （LMRGs） were obtained from the MSigDB database. The samples were randomly divided into training and validation sets at a 1∶1 ratio using the caret package. Univariate Cox analysis was used to identify LMRGs significantly associated with OC prognosis. LASSO-Cox analysis was performed to select model genes for building a prognostic model. The prognostic model was evaluated using Kaplan-Meier curves and receiver operating characteristic （ROC） curves, followed by internal validation using TCGA data. Finally, a column chart was constructed, and immune infiltration analysis was conducted using the CIBERSORT algorithm. Results An 8-gene prognostic model for ovarian cancer was established. Survival analysis showed significant differences in prognosis between the high-risk and low-risk groups （P < 0.05）. The AUC indicated that the model had moderate predictive efficacy. Multivariate Cox analysis demonstrated that LMrisk was an independent prognostic factor for ovarian cancer patients （P < 0.001）. Immune infiltration analysis revealed the association between LMrisk and immune response in ovarian cancer. Conclusion The prognostic model developed in this study can serve as a new tool for evaluating prognosis in ovarian cancer patients. LMrisk may be a robust prognostic biomarker with potential clinical application. -
Key words:
- Ovarian cancer /
- Lipid metabolism /
- Prognostic model /
- Immune cell infiltration
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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
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