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作 者:蒋奕斌 张梦翊 王朋佳 胡泉 苗叶 钱晶[1,2] JIANG Yibin;ZHANG Mengyi;WANG Pengjia;HU Quan;MIAO Ye;QIAN Jing(Huzhou University Schools of Nursing and Medicine,Huzhou University,Huzhou,Zhejiang 313000,China;Zhejiang Key Laboratory of Vector Biology and Pathogen Control,Huzhou,Zhejiang 313000,China)
机构地区:[1]湖州师范学院医学院,浙江湖州313000 [2]浙江省媒介生物学与病原控制重点实验室,浙江湖州313000
出 处:《现代医药卫生》2023年第21期3626-3631,3636,共7页Journal of Modern Medicine & Health
基 金:教育部国家级大学生创新创业训练计划项目(202210347012)。
摘 要:目的通过建立坏死性凋亡相关的长非编码RNA(NR lncRNA)模型,预测肾透明细胞癌(KIRC)患者预后及区分冷、热肿瘤。方法从TCGA数据库中下载KIRC患者基因表达谱及临床信息。通过共表达分析和单变量Cox风险回归分析确定与KIRC预后相关的NR lncRNA。使用Lasso回归分析构建NR lncRNA模型。应用χ^(2)检验和Kaplan-Meier生存分析对NR lncRNA模型进行验证和分析。通过R语言中“ConsensusClusterPlus”包应用NR lncRNA模型区分冷、热肿瘤。结果确定了3种与KIRC患者预后相关的NR lncRNA,并由此构建了NR lncRNA模型。高风险组较低风险组具有不良的预后。KIRC被分为2个亚型。与亚型2比较,亚型1具有免疫抑制性(即冷肿瘤)。结论建立了一个NR lncRNA模型,可预测KIRC患者预后,并为临床试验提供了理论基础。Objective To predict the prognosis of patients with kidney renal clear cell carcinoma(KIRC),and to distinguish between cold and hot tumors by establishing a model of necroptosis-related long non-coding RNA(NR lncRNA).Methods The gene expression profile and its clinical information of KIRC patients were downloaded from TCGA database.The KIRC prognostic related NR lncRNA was determined by co-expression analysis and univariate Cox risk regression analysis.Using the Lasso regression analysis to construct a NR lncRNA model.Theχ^(2) test and Kaplan-Meier survival analysis were used to verify and analyze the NR lncRNA model.The NR lncRNA model was applied to distinguish cold and hot tumors using the“ConsensusClusterPlus”package in R language.Results Three kinds of NR lncRNA related to the prognosis of KIRC patients were identified,and the NR lncRNA model was constructed.The high-risk group had a worse prognosis than the low-risk group.KIRC was divided into two subtypes,and subtpye 1 was immunosuppressive(that was,cold tumor)compared with subtpye 2.Conclusion A model of NR lncRNA is established,which can predict the prognosis of KIRC patients and provide a theoretical basis for clinical trials.
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