Cellular specificity of lactate metabolism and a novel lactate-related gene pair index for frontline treatment in clear cell renal cell carcinoma

• Published in: Frontiers in oncology Vol. 13; p. 1253783
• Main Authors: Li, Xiangsheng, Du, Guangsheng, Li, Liqi, Peng, Ke
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 19.09.2023
• Subjects: gene pair algorithm; immune checkpoint blockade; lactate metabolism; mTOR-targeted therapy; Oncology; single cell analysis
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2023.1253783

Although lactate metabolism-related genes (LMRGs) have attracted attention for their effects on cancer immunity, little is known about their function in clear cell renal cell carcinoma (ccRCC). The aim of this study was to examine the cellular specificity of lactate metabolism and how it affected the first-line treatment outcomes in ccRCC.BackgroundAlthough lactate metabolism-related genes (LMRGs) have attracted attention for their effects on cancer immunity, little is known about their function in clear cell renal cell carcinoma (ccRCC). The aim of this study was to examine the cellular specificity of lactate metabolism and how it affected the first-line treatment outcomes in ccRCC.GSE159115 was used to examine the features of lactate metabolism at the single-cell level. Utilizing the transcriptome, methylation profile, and genomic data from TCGA-KIRC, a multi-omics study of LMRG expression characteristics was performed. A prognostic index based on a gene-pair algorithm was created to assess how LMRGs affected patients' clinical outcomes. To simulate the relationship between the prognostic index and the frontline treatment, pRRophetic and Subclass Mapping were used. E-MTAB-1980, E-MTAB-3267, Checkmate, and Javelin-101 were used for external validation.MethodsGSE159115 was used to examine the features of lactate metabolism at the single-cell level. Utilizing the transcriptome, methylation profile, and genomic data from TCGA-KIRC, a multi-omics study of LMRG expression characteristics was performed. A prognostic index based on a gene-pair algorithm was created to assess how LMRGs affected patients' clinical outcomes. To simulate the relationship between the prognostic index and the frontline treatment, pRRophetic and Subclass Mapping were used. E-MTAB-1980, E-MTAB-3267, Checkmate, and Javelin-101 were used for external validation.The variable expression of some LMRGs in ccRCC can be linked to variations in DNA copy number or promoter methylation levels. Lactate metabolism was active in tumor cells and vSMCs, and LDHA, MCT1, and MCT4 were substantially expressed in tumor cells, according to single-cell analysis. The high-risk patients would benefit from immune checkpoint blockade monotherapy (ICB) and ICB plus tyrosine kinase inhibitors (TKI) therapy, whereas the low-risk individuals responded to mTOR-targeted therapy.ResultsThe variable expression of some LMRGs in ccRCC can be linked to variations in DNA copy number or promoter methylation levels. Lactate metabolism was active in tumor cells and vSMCs, and LDHA, MCT1, and MCT4 were substantially expressed in tumor cells, according to single-cell analysis. The high-risk patients would benefit from immune checkpoint blockade monotherapy (ICB) and ICB plus tyrosine kinase inhibitors (TKI) therapy, whereas the low-risk individuals responded to mTOR-targeted therapy.At the single-cell level, our investigation demonstrated the cellular specificity of lactate metabolism in ccRCC. We proposed that the lactate-related gene pair index might be utilized to identify frontline therapy responders in ccRCC patients as well as predict prognosis.ConclusionsAt the single-cell level, our investigation demonstrated the cellular specificity of lactate metabolism in ccRCC. We proposed that the lactate-related gene pair index might be utilized to identify frontline therapy responders in ccRCC patients as well as predict prognosis.