基于TCGA数据库肾透明细胞癌代谢相关基因预后模型构建及其评估

李德峰; 毛杨; 王文静; 李园园

doi:10.16016/j.2097-0927.202206121

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基于TCGA数据库肾透明细胞癌代谢相关基因预后模型构建及其评估

临床医学 | 更新时间：2026-01-09

- 基于TCGA数据库肾透明细胞癌代谢相关基因预后模型构建及其评估
- Construction and evaluation of a prognostic model for renal clear cell carcinoma based on metabolism-related genes in TCGA database
- 陆军军医大学学报 2023年45卷第2期页码：165-172
- 作者机构：
  
  400037 重庆，陆军军医大学(第三军医大学)第二附属医院临床医学研究中心
- 作者简介：
  
  [ 李园园，E-mail: 419914836@qq.com LI Yuanyuan, E-mail: 419914836@qq.com
- 基金信息：
  
  国家自然科学基金面上项目(31872634)
- DOI：10.16016/j.2097-0927.202206121
  中图分类号： R318.04;R394.3;R737.11
- 收稿：2022-06-20，
  
  修回：2022-09-20，
  
  纸质出版：2023-01-30
- 稿件说明：
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李德峰, 毛杨, 王文静, 等. 基于TCGA数据库肾透明细胞癌代谢相关基因预后模型构建及其评估[J]. 陆军军医大学学报, 2023,45(2):165-172.

Defeng LI, Yang MAO, Wenjing WANG, et al. Construction and evaluation of a prognostic model for renal clear cell carcinoma based on metabolism-related genes in TCGA database[J]. Journal of Army Medical University, 2023, 45(2): 165-172.
李德峰, 毛杨, 王文静, 等. 基于TCGA数据库肾透明细胞癌代谢相关基因预后模型构建及其评估[J]. 陆军军医大学学报, 2023,45(2):165-172. DOI： 10.16016/j.2097-0927.202206121.

Defeng LI, Yang MAO, Wenjing WANG, et al. Construction and evaluation of a prognostic model for renal clear cell carcinoma based on metabolism-related genes in TCGA database[J]. Journal of Army Medical University, 2023, 45(2): 165-172. DOI： 10.16016/j.2097-0927.202206121.

摘要

目的

探讨代谢相关基因在肾透明细胞癌(kidney renal clear cell carcinoma，KIRC)中的表达及与患者预后的关系，寻找潜在干预靶点。

方法

利用生物信息学方法挖掘癌症基因图谱(Cancer Genome Atlas Program，TCGA)数据库中KIRC转录组数据，寻找在癌组织中异常表达的代谢相关基因(metabolism-related genes

MRGs)，通过LASSO和Cox回归筛选与患者总生存期密切相关的代谢基因，进一步Cox回归构建患者预后风险评分模型并评价其预测能力。采用实时荧光定量PCR验证预后相关MRGs在人肾透明细胞癌A498细胞及肾小管上皮细胞HK-2的表达特征。

结果

在KIRC与癌旁组织中差异表达的代谢相关基因共875个。单因素Cox回归分析显示38个MRGs显著影响患者预后，LASSO及多因素Cox回归分析进一步筛选出7个基因(

CNGB

1、

CACNA

、

KCNT

1、

KCNH

1、

PLA

、

CHST

6、

ATP

1)作为预后危险因素用于风险评分模型构建。患者风险评分可作为独立危险因子预测KIRC患者预后(风险比为4.671，

0.001)，而患者年龄、性别、肿瘤分级则与患者预后无相关性(

0.05)。

结论

KIRC中多个MRGs存在异常表达，从中筛选到7个基因并构建的代谢相关预后风险模型，可以有效预测KIRC患者预后，为KIRC临床诊疗提供新依据。

Abstract

Objective

To investigated the expression of metabolism-related genes (MRGs) in kidney renal clear cell carcinoma (KIRC) and its correlation with prognosis of patients so as to search for potential intervention targets.

Methods

Bioinformatics were used to mine KIRC transcriptome data in the Cancer Genome Atlas Program (TCGA) database

and the MRGs abnormally expressed were obtained

among which the ones closely associated with overall survival of KIRC patients were screened by LASSO and Cox regression. Furthermore

Cox regression analysis was adopted to construct a risk-score model to predict the prognosis of KIRC patients

and its predictive ability was evaluated. Finally

the expression characteristics of prognostic related MRGs were verified using RT-qPCR in KIRC cell line A498 and renal tubular epithelial cell line HK-2

respectively.

Results

A total of 875 differentially expressed MRGs were found between KIRC and adjacent tissues. Univariate Cox regression analysis showed that 38 MRGs significantly affected the prognosis of patients

and 7 of them (

CNGB

CACNA

KCNT

KCNH

PLA

CHST

and

ATP

1) were further selected as prognostic risk factors by LASSO and multivariate Cox regression. Subsequently

a risk-score model was constructed based on the above 7 factors. The score obtained from the model could be used as an independent risk factor to predict the prognosis of KIRC patients (risk ratio: 4.671

0.001)

while age

gender

and tumor grade were not correlated with the prognosis of patients (

0.05).

Conclusion

There are several MRGs with abnormal expression in KIRC

and the prognostic risk model based on 7 genes screened from the MRGs can effectively predict the prognosis of KIRC patients

providing a new basis for future diagnosis and treatment of KIRC.

关键词

Keywords

references

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