Wang Zheng, Huangfu Zhao, Liu Tao, Li Yuan, Gao Yuchen, Gan Xinxin, Wu Xiaofeng, Chen Shu, Li Xiaomin, Wang Linhui, Gao Xiaofeng
Department of Urology, Changhai Hospital, Naval Medical University Shanghai 200433, China.
School of Health Science and Engineering, University of Shanghai for Science and Technology Shanghai 200433, China.
Am J Cancer Res. 2025 May 15;15(5):2222-2242. doi: 10.62347/VUZH4644. eCollection 2025.
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, P = 0.032), trigonelline (overall RCC: OR = 1.25, P = 0.049), and gamma-glutamylthreonine (chRCC: OR = 2.90, P = 0.012). KEGG analysis revealed significant enrichment in the valine, leucine, and isoleucine biosynthesis pathway for ccRCC (P = 1.2 × 10), and pyrimidine metabolism for chRCC (P = 6.5 × 10). 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.
代谢失调是肾癌的一个标志,但其主要亚型中特定代谢物的因果作用仍不清楚。本研究旨在阐明循环代谢物与肾癌的三种主要亚型——透明细胞肾细胞癌(ccRCC)、乳头状肾细胞癌(pRCC)和嫌色性肾细胞癌(chRCC)之间的因果关系,并确定潜在的诊断和治疗靶点。总共评估了1400种循环代谢物和代谢比值作为暴露因素,肾癌结局数据来自芬兰基因数据库。从全基因组关联研究(GWAS)中选择遗传工具,并与结局数据进行整合。采用逆方差加权(IVW)方法作为主要方法进行孟德尔随机化(MR)分析,并辅以多种敏感性分析,包括 Cochr an's Q检验、MR-Egger回归、留一法分析和MR-PRESSO。为校正多重检验,将代谢物分为绝对水平和代谢比值,并在每个类别中分别应用Benjamini-Hochberg错误发现率(FDR)程序。通过KEGG通路富集进一步分析因果相关的代谢物。为进行临床验证,对48例ccRCC患者的配对肿瘤组织和相邻正常组织进行了非靶向代谢组学分析。总共发现85种代谢物与肾癌存在因果关联,其中ccRCC有57种,pRCC有71种,chRCC有51种。经过FDR校正后,三种代谢物仍具有统计学意义:肉碱(总体肾癌:OR = 1.25,P = 0.032)、胡芦巴碱(总体肾癌:OR = 1.25,P = 0.049)和γ-谷氨酰苏氨酸(chRCC:OR = 2.90,P = 0.012)。KEGG分析显示,ccRCC在缬氨酸、亮氨酸和异亮氨酸生物合成途径中显著富集(P = 1.2×10),chRCC在嘧啶代谢途径中显著富集(P = 6.5×10)。ccRCC组织的代谢组学分析证实了7种代谢物水平异常,包括2-羟基戊二酸升高(倍数变化[FC] = 3.1,P = 0.001)和柠檬酸降低(FC = 0.4,P = 0.001),两者均与疾病进展相关。总之,这项综合研究确定肉碱和胡芦巴碱可能是肾癌进展的促成因素,而γ-谷氨酰苏氨酸似乎特别参与chRCC的发病机制。此外,ccRCC中鞘氨醇-1-磷酸、乙酰肉碱、γ-谷氨酰谷氨酰胺和N-乙酰胞苷表达的改变突出了关键的代谢紊乱,并强调了它们作为肾癌新型生物标志物和治疗靶点的潜力。