An eleven metabolic gene signature-based prognostic model for clear cell renal cell carcinoma.

An eleven metabolic gene signature-based prognostic model for clear cell renal cell carcinoma.
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基于十一个代谢基因特征的透明细胞肾细胞癌预后模型

DOI:
10.18632/aging.104088
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发表时间:
2020-11-18
期刊:
Aging
影响因子:
--
通讯作者:
Wang T
Wang T
中科院分区:
其他
文献类型:
--
作者:
Wu Y;Wei X;Feng H;Hu B;Liu B;Luan Y;Ruan Y;Liu X;Liu Z;Wang S;Liu J;Wang T

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本研究利用TCGA数据库中539例透明细胞肾细胞癌(clear cell renal cell carcinoma, ccRCC)和72例正常肾组织的转录组数据,进行生物信息学和统计学分析,探讨代谢基因在透明细胞肾细胞癌(clear cell renal cell carcinoma, ccRCC)中的预后意义。我们在ccRCC组织中发现了79个上调和45个下调的代谢基因(n=124)。通过进一步分析鉴定出11个预后代谢基因(NOS1、ALAD、ALDH3B2、ACADM、ITPKA、IMPDH1、SCD5、FADS2、ACHE、CA4和HK3)。然后,我们构建了一个基于11个代谢基因特征的预后风险评分模型,并将ccRCC患者分为高危组和低危组。高危ccRCC患者的总生存期(OS)明显短于低危ccRCC患者。预后风险评分模型的受试者工作特征(ROC)曲线分析显示,1年、3年和5年OS的ROC曲线下面积分别为0.810、0.738和0.771。因此,我们的预后模型在TCGA和E-MTAB-1980 ccRCC患者队列中显示出良好的预测能力。我们还建立了基于这11个代谢基因的nomogram,并在TCGA队列中进行了内部验证,显示了对ccRCC患者预后的准确预测。
In this study, we performed bioinformatics and statistical analyses to investigate the prognostic significance of metabolic genes in clear cell renal cell carcinoma (ccRCC) using the transcriptome data of 539 ccRCC and 72 normal renal tissues from TCGA database. We identified 79 upregulated and 45 downregulated (n=124) metabolic genes in ccRCC tissues. Eleven prognostic metabolic genes (NOS1, ALAD, ALDH3B2, ACADM, ITPKA, IMPDH1, SCD5, FADS2, ACHE, CA4, and HK3) were identified by further analysis. We then constructed an 11-metabolic gene signature-based prognostic risk score model and classified ccRCC patients into high- and low-risk groups. Overall survival (OS) among the high-risk ccRCC patients was significantly shorter than among the low-risk ccRCC patients. Receiver operating characteristic (ROC) curve analysis of the prognostic risk score model showed that the areas under the ROC curve for the 1-, 3-, and 5-year OS were 0.810, 0.738, and 0.771, respectively. Thus, our prognostic model showed favorable predictive power in the TCGA and E-MTAB-1980 ccRCC patient cohorts. We also established a nomogram based on these eleven metabolic genes and validated internally in the TCGA cohort, showing an accurate prediction for prognosis in ccRCC.
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