More Tushar H, Hiller Karsten, Seifert Martin, Illig Thomas, Schmidt Rudi, Gronauer Raphael, von Hahn Thomas, Weilert Hauke, Stang Axel
Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.
Asklepios Precision Medicine, Asklepios Hospitals GmbH & Co KgaA, Königstein (Taunus), Germany.
Front Oncol. 2024 Feb 20;14:1286896. doi: 10.3389/fonc.2024.1286896. eCollection 2024.
Cachexia is a body wasting syndrome that significantly affects well-being and prognosis of cancer patients, without effective treatment. Serum metabolites take part in pathophysiological processes of cancer cachexia, but apart from altered levels of select serum metabolites, little is known on the global changes of the overall serum metabolome, which represents a functional readout of the whole-body metabolic state. Here, we aimed to comprehensively characterize serum metabolite alterations and analyze associated pathways in cachectic cancer patients to gain new insights that could help instruct strategies for novel interventions of greater clinical benefit.
Serum was sampled from 120 metastatic cancer patients (stage UICC IV). Patients were grouped as cachectic or non-cachectic according to the criteria for cancer cachexia agreed upon international consensus (main criterium: weight loss adjusted to body mass index). Samples were pooled by cachexia phenotype and assayed using non-targeted gas chromatography-mass spectrometry (GC-MS). Normalized metabolite levels were compared using -test (p < 0.05, adjusted for false discovery rate) and partial least squares discriminant analysis (PLS-DA). Machine-learning models were applied to identify metabolite signatures for separating cachexia states. Significant metabolites underwent MetaboAnalyst 5.0 pathway analysis.
Comparative analyses included 78 cachectic and 42 non-cachectic patients. Cachectic patients exhibited 19 annotable, significantly elevated (including glucose and fructose) or decreased (mostly amino acids) metabolites associating with aminoacyl-tRNA, glutathione and amino acid metabolism pathways. PLS-DA showed distinct clusters (accuracy: 85.6%), and machine-learning models identified metabolic signatures for separating cachectic states (accuracy: 83.2%; area under ROC: 88.0%). We newly identified altered blood levels of erythronic acid and glucuronic acid in human cancer cachexia, potentially linked to pentose-phosphate and detoxification pathways.
We found both known and yet unknown serum metabolite and metabolic pathway alterations in cachectic cancer patients that collectively support a whole-body metabolic state with impaired detoxification capability, altered glucose and fructose metabolism, and substrate supply for increased and/or distinct metabolic needs of cachexia-associated tumors. These findings together imply vulnerabilities, dependencies and targets for novel interventions that have potential to make a significant impact on future research in an important field of cancer patient care.
恶病质是一种严重影响癌症患者生活质量和预后的身体消耗综合征，目前尚无有效治疗方法。血清代谢物参与癌症恶病质的病理生理过程，但除了某些血清代谢物水平的改变外，对于代表全身代谢状态功能读数的整体血清代谢组的全局变化知之甚少。在此，我们旨在全面表征恶病质癌症患者血清代谢物的改变，并分析相关途径，以获得新的见解，从而有助于指导具有更大临床益处的新型干预策略。
从120例转移性癌症患者（国际抗癌联盟IV期）中采集血清。根据国际共识认可的癌症恶病质标准（主要标准：根据体重指数调整体重减轻情况）将患者分为恶病质组或非恶病质组。样本按恶病质表型进行合并，并使用非靶向气相色谱 - 质谱联用仪（GC-MS）进行检测。使用t检验（p < 0.05，经错误发现率调整）和偏最小二乘判别分析（PLS-DA）比较标准化代谢物水平。应用机器学习模型识别区分恶病质状态的代谢物特征。对显著的代谢物进行MetaboAnalyst 5.0途径分析。
比较分析纳入了78例恶病质患者和42例非恶病质患者。恶病质患者表现出19种可注释的、显著升高（包括葡萄糖和果糖）或降低（主要是氨基酸）的代谢物，这些代谢物与氨酰 - tRNA、谷胱甘肽和氨基酸代谢途径相关。PLS-DA显示出明显的聚类（准确率：85.6%），机器学习模型识别出区分恶病质状态的代谢特征（准确率：83.2%；ROC曲线下面积：88.0%）。我们首次发现人类癌症恶病质中赤藓糖酸和葡萄糖醛酸的血液水平发生改变，这可能与磷酸戊糖途径和解毒途径有关。
我们在恶病质癌症患者中发现了已知和未知的血清代谢物及代谢途径改变，这些改变共同支持了一种全身代谢状态，即解毒能力受损、葡萄糖和果糖代谢改变，以及为恶病质相关肿瘤增加和/或独特的代谢需求提供底物供应。这些发现共同暗示了新干预措施的脆弱性、依赖性和靶点，有可能对癌症患者护理这一重要领域的未来研究产生重大影响。
