Construction of prognostic risk model for renal cell carcinoma based on lactate metabolism-related genes and analysis of immune characteristics of renal cell carcinoma
10.3760/cma.j.cn112309-20240902-00316
- VernacularTitle:基于乳酸代谢相关基因构建肾细胞癌预后风险模型并分析其免疫特征
- Author:
Zhijia SUN
1
;
Zhuo SONG
1
;
Xu LIU
1
;
Xiaoli KANG
1
;
Xinji LI
1
;
Yingjie WANG
1
Author Information
1. 空军军医大学空军特色医学中心放射治疗科，北京 100142
- Publication Type:Journal Article
- Keywords:
Lactic acid;
Renal cell carcinoma;
Risk model;
Immune characteristics
- From:
Chinese Journal of Microbiology and Immunology
2025;45(11):949-957
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a prognostic risk model based on lactate metabolism-related genes screened using bioinformatics methods in renal cell carcinoma patients，and investigate the clinical prognosis and immune characteristics of renal cell carcinoma.Methods:Gene expression data and clinical information of patients with renal cell carcinoma were downloaded from the Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma（TCGA-KIRC）dataset. Lactate metabolism-related gene sets were obtained from the Gene Set Enrichment Analysis（GSEA）database. The R package DEseq2 was employed to identify differentially expressed genes associated with lactate metabolism within the TCGA-KIRC dataset. GO and KEGG enrichment analyses were performed using the clusterProfiler package. Prognosis-related genes were screened via univariate Cox regression analysis and the intersection with lactate metabolism-related differentially expressed genes was obtained. A risk model was constructed using LASSO regression followed by multivariate Cox regression analysis to calculate risk scores. This risk model was subsequently validated using the GSE29609 dataset. Patients were stratified into high-risk and low-risk groups based on the median risk score. The expression profiles of key immune molecule genes and immune checkpoint genes were compared between the two groups. Survival analysis curves for immune checkpoint genes were generated using the survival and survminer R packages. Differences in tumor mutation burden（TMB）between the high-risk and low-risk groups were assessed，and corresponding TMB survival analysis curves were plotted. Finally，the tumor immune dysfunction and exclusion（TIDE）algorithm was used to evaluate disparities in immunotherapy response potential between the two risk groups.Results:An optimal prognostic risk model incorporating seven lactate metabolism- and prognosis-related genes（ LDHD，PER2，ACADM，FLI1，LIPA，TCIRG1，SLC25A4）was constructed and successfully validated in the GSE29609 dataset. Univariate Cox regression analysis revealed that a high-risk score was significantly associated with poor prognosis（ HR=2.915，95% CI：2.451-3.470， P<0.001）. Multivariate Cox regression analysis confirmed that this risk model could serve as an independent prognostic factor for patients with renal cell carcinoma（ HR=2.231，95% CI：1.829-2.722， P<0.001）. Patients in the high-risk group exhibited significantly worse outcomes compared to the low-risk group，regardless of whether they had early-stage or advanced-stage renal cell carcinoma（both P<0.001）. Analyses related to the immune microenvironment indicated an upregulated immunosuppressive phenotype in the high-risk group. Furthermore，the TMB was significantly higher in the high-risk group than in the low-risk group（ P=0.032），and patients within the high-risk group exhibiting higher TMB levels demonstrated even poorer survival（ P<0.001）. Finally，the TIDE score was significantly elevated in the high-risk group in comparison to the low-risk group（ P<0.001）. Conclusions:The risk model based on lactate metabolism-related genes constructed in this study can guide the prognosis of renal cell carcinoma. Patients in the high-risk group are more prone to immune escape and formation of an inhibitory immune microenvironment，leading to worse prognoses. This risk model may serve as a biomarker for predicting immunotherapy response.
