Renal cell carcinoma (RCC) is a malignant tumor that currently lacks clinically useful biomarkers indicative of early diagnosis or disease status. RCC has commonly been diagnosed based on imaging results. Metabolomics offers a potential technology for discovering biomarkers and therapeutic targets by comprehensive screening of metabolites from patients with various cancers. We aimed to identify metabolites associated with early diagnosis and clinicopathological factors in RCC using global metabolomics (G-Met). Tumor and nontumor tissues were sampled from 20 cases of surgically resected clear cell RCC. G-Met was performed by liquid chromatography mass spectrometry and important metabolites specific to RCC were analyzed by multivariate statistical analysis for cancer diagnostic ability based on area under the curve (AUC) and clinicopathological factors (tumor volume, pathological T stage, Fuhrman grade, presence of coagulation necrosis and distant metastasis). We identified 58 metabolites showing significantly increased levels in tumor tissues, 34 of which showed potential early diagnostic ability (AUC >0.8), but 24 did not discriminate between tumor and nontumor tissues (AUC ≤0.8). We recognized 6 pathways from 9 metabolites with AUC >0.8 and 7 pathways from 10 metabolites with AUC ≤0.8 about malignant status. Clinicopathological factors involving malignant status correlated significantly with metabolites showing AUC ≤0.8 (p = 0.0279). The tricarboxylic acid cycle (TCA) cycle, TCA cycle intermediates, nucleotide sugar pathway and inositol pathway were characteristic pathways for the malignant status of RCC. In conclusion, our study found that metabolites and their pathways allowed discrimination between early diagnosis and malignant status in RCC according to our G-Met protocol.
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