Wu Jun, Wu Yuqian, Sun Yefeng, You Jianhang, Zhang Wenjie, Zhao Tao
Precision Medicine Center of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Department of Medical Oncology, People's Hospital of Rizhao, Rizhao, 276826, China.
Discov Oncol. 2025 Jun 7;16(1):1024. doi: 10.1007/s12672-025-02746-2.
Clear cell renal cell carcinoma (ccRCC) is the most prevalent and highly aggressive subtype of kidney cancer. Despite the progress in research, the roles of lactate metabolism and immune-related genes (LMRGs) in its prognosis and immune microenvironment remain unclear. Until now, no studies have explored the potential impact of LMRGs on the prognosis of ccRCC and their relationship with the tumor immune microenvironment.
Transcriptomic analysis was carried out using the TCGA and GEO databases. Non-negative matrix factorization (NMF) was used to subtype ccRCC samples. The Cox proportional hazards regression model and the LASSO algorithm were combined to screen the core genes related to prognosis. The Kaplan-Meier survival analysis was used to assess the relationship between these genes and patient survival. The CIBERSORT and ESTIMATE algorithms were used to analyze the level of immune infiltration.
Using NMF analysis, ccRCC samples were classified into two subtypes. Kaplan-Meier survival analysis revealed that patients in Cluster 2 exhibited a better prognosis than those in Cluster 1. LASSO regression analysis identified five key genes-STAT2, PDGFRL, APLNR, PRKCQ, and THRB-which were subsequently used to construct a prognostic model. The survival rate in the high-risk group was significantly lower than that in the low-risk group. Immune microenvironment analysis demonstrated that the high-risk group exhibited higher immune cell infiltration, while the low-risk group was enriched for metabolism-related pathways. Tumor mutation burden (TMB) analysis indicated that TMB synergized with the risk score. Finally, the prognostic value of these key genes was validated using the K-M database.
Lactate metabolism and immune-related genes are of great significance in the prognostic evaluation of ccRCC. The core genes screened based on these mechanisms have the potential value as biomarkers.
透明细胞肾细胞癌（ccRCC）是最常见且侵袭性很强的肾癌亚型。尽管研究取得了进展，但乳酸代谢和免疫相关基因（LMRGs）在其预后和免疫微环境中的作用仍不明确。到目前为止，尚无研究探讨LMRGs对ccRCC预后的潜在影响及其与肿瘤免疫微环境的关系。
使用TCGA和GEO数据库进行转录组分析。采用非负矩阵分解（NMF）对ccRCC样本进行亚型分类。结合Cox比例风险回归模型和LASSO算法筛选与预后相关的核心基因。采用Kaplan-Meier生存分析评估这些基因与患者生存之间的关系。使用CIBERSORT和ESTIMATE算法分析免疫浸润水平。
通过NMF分析，将ccRCC样本分为两个亚型。Kaplan-Meier生存分析显示，第2组患者的预后优于第1组。LASSO回归分析确定了五个关键基因——STAT2、PDGFRL、APLNR、PRKCQ和THRB，随后用于构建预后模型。高危组的生存率显著低于低危组。免疫微环境分析表明，高危组表现出更高的免疫细胞浸润，而低危组则富含代谢相关途径。肿瘤突变负荷（TMB）分析表明，TMB与风险评分协同作用。最后，使用K-M数据库验证了这些关键基因的预后价值。
乳酸代谢和免疫相关基因在ccRCC的预后评估中具有重要意义。基于这些机制筛选出的核心基因具有作为生物标志物的潜在价值。
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