| 引用本文: | 李艳,桂子玮,李希捷,王勇琦,牛晓辰.肾透明细胞癌患者铁死亡相关基因的预后作用[J].生物信息学,2022,20(3):173-181. |
| LI Yan,GUI Ziwei,LI Xijie,WANG Yongqi,NIU Xiaochen.Prognostic effect of ferroptosis-related genes in patients with kidney renal clear cell carcinoma[J].Chinese Journal of Bioinformatics,2022,20(3):173-181. |
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| 摘要: |
| 探讨铁死亡相关基因在肾透明细胞癌患者中的表达及其预后价值。通过TCGA数据库下载KIRC的相关测序数据与检索到的铁死亡相关基因取交集,进行铁死亡相关基因的差异分析。之后利用单变量和多变量Cox回归分析,筛选具有预后价值的基因,构建预测患者生存情况的风险评分模型,并对模型进行验证。对高低风险组进行GO与KEGG通路富集,探讨风险差异的可能原因；通过ssGSEA分析,评估高低风险组间的免疫浸润情况。在KIRC患者的肿瘤组织和正常组织中,共得到21个差异的铁死亡相关基因；通过单因素Cox回归分析,获得 28 个与KIRC预后相关的基因；之后进行Lasso回归与多因素Cox回归分析,结果显示有10个基因被纳入模型,计算公式为:风险值(Risk score)=(0.024 5)×ALOX5表达值+(0.126 0)×CBS表达值+(0.199 5)×CD44表达值+(0.218 3)×CHAC1表达值+(-0.295 9)×HMGCR表达值+(0.036 7)×MT1G表达值+(0.061 4)×SLC7A11表达值+(-0.080 7)×FDFT1表达值+(0.160 3)×PEBP1表达值+(-0.220 5)×GOT1表达值。生存状态图表明,高风险组死亡病例数多于低风险组；ROC曲线表明风险评分模型具备一定预测能力；K-M生存分析显示,高风险组总体生存率低于低风险组(P=5.73×10-13)。GO与KEGG富集分析提示,高低风险组间免疫情况及IL-17信号通路存在显著差异；进一步的ssGSEA富集显示,高低风险组间大部分免疫细胞的评分存在显著差异。基于铁死亡相关基因的预后风险评分模型可用于KIRC的预后预测,针对铁死亡相关基因设计靶点可能是治疗KIRC的一种新选择。 |
| 关键词: 肾透明细胞癌 铁死亡 预后模型 TCGA数据库 |
| DOI：10.12113/202104009 |
| 分类号:R737.11 |
| 文献标识码:A |
| 基金项目:山西省2019年大学生创新创业训练计划项目(No.2019189). |
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| Prognostic effect of ferroptosis-related genes in patients with kidney renal clear cell carcinoma |
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LI Yan1, GUI Ziwei1, LI Xijie2, WANG Yongqi1, NIU Xiaochen3
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(1. The second Clinical Medical College of Shanxi Medical University, Taiyuan 030001, China; 2. Basic Medical College of Shanxi Medical University, Taiyuan 030001, China; 3.The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, China)
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| Abstract: |
| To explore the expression and prognostic value of ferroptosis-related genes in patients with kidney renal clear cell carcinoma(KIRC),relevant sequencing data of KIRC were downloaded from the TCGA database and intersected with retrieved ferroptosis-related genes for difference analysis. Then, univariate and multivariate Cox regression analyses were used for screening the genes with prognostic value.The risk scoring model was constructed to predict the survival of patients, and the model was verified. The high and low risk groups were enriched with GO and KEGG pathways to explore the possible reasons for the risk differences.The ssGSEA analysis was used to assess the level of immune infiltration between the high and low risk groups. In the tumor tissues and normal tissues of KIRC patients, a total of 21 differential ferroptosis-related genes were obtained. Through univariate Cox regression analysis, 28 genes related to the prognosis of KIRC were obtained. Then Lasso regression and multivariate Cox regression analysis were performed,and results showed that ten genes were included in the model. The calculation formula was: risk score=(0.024 5)×ALOX5 expression value+(0.126 0)×CBS expression value+(0.199 5)×CD44 expression value+(0.218 3)×CHAC1 expression value+(-0.295 9)×HMGCR expression value+(0.036 7)×MT1G expression value+(0.061 4)×SLC7A11 expression value+(-0.080 7)×FDFT1 expression value+(0.160 3)×PEBP1 expression value+(-0.220 5)×GOT1 express value. The survival status diagram showed that the high risk group had more deaths than the low risk group. The ROC curve showed that the risk scoring model had certain predictive ability. K-M survival analysis showed that the overall survival rate of the high risk group was lower than that of the low risk group (P=5.73×10-13). The GO and KEGG enrichment analyses showed that there were significant differences in the immune status and IL-17 signaling pathway between the high and low risk groups. Further ssGSEA enrichment showed that there were significant differences in the scores of most immune cells between the high and low risk groups. The prognostic risk scoring model based on ferroptosis-related genes can be used to predict the prognosis of KIRC, and designing targets for ferroptosis-related genes may be a new option for the treatment of KIRC. |
| Key words: Kidney renal clear cell carcinoma Ferroptosis Prognostic model TCGA database |
