Am J Cancer Res. 2025 ;15(5): 2222-2242
Zheng Wang,
Zhao Huangfu,
Tao Liu,
Yuan Li,
Yuchen Gao,
Xinxin Gan,
Xiaofeng Wu,
Shu Chen,
Xiaomin Li,
Linhui Wang,
Xiaofeng Gao.
Metabolic dysregulation is a hallmark of kidney cancer, yet the causal roles of specific metabolites in its major subtypes remain unclear. This study aimed to elucidate the causal relationships between circulating metabolites and the three primary subtypes of kidney cancer - clear cell renal cell carcinoma (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) - and to identify potential diagnostic and therapeutic targets. A total of 1,400 circulating metabolites and metabolic ratios were evaluated as exposures, with kidney cancer outcomes derived from the FinnGen database. Genetic instruments were selected from genome-wide association studies (GWAS) and harmonized with outcome data. Mendelian randomization (MR) analyses were conducted using the inverse-variance weighted (IVW) method as the primary approach, supported by multiple sensitivity analyses, including Cochran's Q test, MR-Egger regression, leave-one-out analysis, and MR-PRESSO. To correct for multiple testing, metabolites were stratified into absolute levels and metabolic ratios, and the Benjamini-Hochberg false discovery rate (FDR) procedure was applied separately within each category. Causally associated metabolites were further analyzed via KEGG pathway enrichment. For clinical validation, untargeted metabolomic profiling was performed on paired tumors and adjacent normal tissues from 48 patients with ccRCC. In total, 85 metabolites were found to be causally associated with kidney cancer, including 57 for ccRCC, 71 for pRCC, and 51 for chRCC. After FDR correction, three metabolites remained statistically significant: carnitine (overall RCC: OR = 1.25, PFDR = 0.032), trigonelline (overall RCC: OR = 1.25, PFDR = 0.049), and gamma-glutamylthreonine (chRCC: OR = 2.90, PFDR = 0.012). KEGG analysis revealed significant enrichment in the valine, leucine, and isoleucine biosynthesis pathway for ccRCC (P = 1.2 × 10-5), and pyrimidine metabolism for chRCC (P = 6.5 × 10-6). Metabolomic profiling of ccRCC tissues confirmed aberrant levels of seven metabolites, including elevated 2-hydroxyglutarate (fold change [FC] = 3.1, P = 0.001) and reduced citrate (FC = 0.4, P = 0.001), both associated with disease progression. In conclusion, this integrative study identified carnitine and trigonelline as potential contributors to RCC progression, while gamma-glutamylthreonine appears to be specifically involved in chRCC pathogenesis. Additionally, altered expression of sphingosine 1-phosphate, acetylcarnitine, gamma-glutamylglutamine, and N-acetylcytidine in ccRCC highlights key metabolic disruptions and underscores their potential as novel biomarkers and therapeutic targets in kidney cancer.
Keywords: Mendelian randomization (MR); Metabolomics; carnitine; clear cell renal cell carcinoma (ccRCC); kidney cancer; metabolic pathways; sphingosine