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。
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.
作者
王兴粉
邓玥
杨丽华
WANG Xingfen;DENG Yue;YANG Lihua(Dept.of Gynecology,The 2nd Affiliated Hospital of Kunming Medical University,Kunming Yunnan 650101,China)
出处
《昆明医科大学学报》
CAS
2024年第4期17-25,共9页
Journal of Kunming Medical University
基金
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)。
关键词
卵巢癌
脂质代谢
预后模型
免疫细胞浸润
Ovarian cancer
Lipid metabolism
Prognostic model
Immune cell infiltration
