Analysis of immune status and prognostic model incorporating lactate metabolism and immune-related genes in clear cell renal cell carcinoma

Jun Wu; Yuqian Wu; Yefeng Sun; Jianhang You; Wenjie Zhang; Tao Zhao

doi:10.1007/s12672-025-02746-2

Analysis of immune status and prognostic model incorporating lactate metabolism and immune-related genes in clear cell renal cell carcinoma

Discov Oncol. 2025 Jun 7;16(1):1024. doi: 10.1007/s12672-025-02746-2.

Authors

Jun Wu^#^{1

2}, Yuqian Wu^#², Yefeng Sun³, Jianhang You⁴, Wenjie Zhang⁵, Tao Zhao⁶

Affiliations

¹ Precision Medicine Center of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
² Department of Medical Oncology, People's Hospital of Rizhao, Rizhao, 276826, China.
³ Department of Emergency, People's Hospital of Rizhao, Rizhao, 276826, China.
⁴ School of Clinical Medicine, Shandong Second Medical University, Weifang, 261053, China.
⁵ Department of Medical Oncology, People's Hospital of Rizhao, Rizhao, 276826, China. 71674818@qq.com.
⁶ Department of Central Laboratory, Shandong Provincial Key Medical and Health Laboratory of The People's Hospital of Rizhao, Rizhao Key Laboratory of Basic Research on Anesthesia and Respiratory Intensive Care, The People's Hospital of Rizhao, Rizhao, 276826, Shandong, China. zttlwj@126.com.

^# Contributed equally.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Clear cell renal cell carcinoma; Lactate metabolism and immune-related genes; Prognostic signature; Tumor microenvironment.