非小细胞肺癌与非癌症人群的代谢组学分析：一项病例对照研究

Lung cancer is the second most common cancer in the United States, with an estimated 124,730 deaths projected in 2025. Non-small cell lung cancer (NSCLC) accounts for 80-85% of all lung cancer cases, making it the predominant form of the disease. 27.4-28.1% of NSCLC cases are diagnosed at early stages, indicating the need for biomarker testing, which enhances detection accuracy beyond imaging-based approaches alone. Metabolomics enables the comprehensive identification of NSCLC-specific metabolites, revealing distinct biochemical alterations that inform the discovery of novel diagnostic biomarkers and could be utilized to further investigate outcomes and therapeutic targets. Therefore, we conducted a pilot metabolomics study using a case-control design to identify differentially abundant metabolites between NSCLC patients and non-cancer controls, with the goal of discovering potential metabolomic biomarkers associated with NSCLC. A total of 80 NSCLC patients (45.6% male; mean age=65.4) and 40 non-cancer control individuals (24.3% male; mean age=40.2) were included in this study. Global metabolomic profiling from serum samples of the study population utilizing the Orbitrap Fusion and the TSQ Altis QqQ mass spectrometry interfaced with the Vanquish Horizon UHPLC system at the UF ICBR Proteomics & Mass Spectrometry. Differentially abundant metabolites were determined using Mann-Whitney U-tests with Benjamini-Hochberg FDR correction and fold-change threshold. Global metabolomic profiling identified 5,307 metabolites, of which 626 (11.8%) were differentially abundant between NSCLC patients and non-cancer controls using stringent criteria (FDR<0.01, FC≥4). 455 metabolites were less abundant, and 171 were more abundant in NSCLC. The results from PCA demonstrated a separation between NSCLC patients and non-cancer controls, with PC1 and PC2 accounting for 25% of the total variance. OPLS-DA confirmed the discrimination of metabolic profiles between NSCLC patients and non-cancer controls. Network analysis revealed dysregulation in metabolic pathways, including the urea cycle, TCA cycle, amino acid metabolism, and polyamine biosynthesis. Key metabolites, including hypoxanthine, adipic acid, and betaine, were significantly less abundant in NSCLC. Pathway enrichment analysis identified enrichment in purine and pyrimidine metabolism, suggesting altered nucleotide metabolism associated with NSCLC. Larger prospective studies are warranted to validate these metabolomic biomarkers for clinical application in NSCLC. Future investigations will evaluate their utility in predicting prognosis, treatment response, and survival. The implementation of these NSCLC-specific metabolomic signatures in clinical practice could enhance prognostic accuracy and inform personalized treatment strategies, ultimately improving outcomes and survival.