Department of Nursing, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, 321000, China.
Central Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, 321000, China.
BMC Cancer. 2024 Sep 27;24(1):1195. doi: 10.1186/s12885-024-12964-6.
Although malnutrition is common in cancer patients, its molecular mechanisms has not been fully clarified. This study aims to identify significantly differential metabolites, match the corresponding metabolic pathways, and develop a predictive model of malnutrition in patients with gastric cancer.
In this cross-sectional study, we applied non-targeted metabolomics using liquid chromatography-mass spectrometry to explore the serum fingerprinting of malnutrition in patients with gastric cancer. Malnutrition-specific differential metabolites were identified by orthogonal partial least-squares discriminant analysis and t-test and matched with the Human Metabolome Database and the LIPID Metabolites and Pathways Strategy. We matched the corresponding metabolic pathways of malnutrition using pathway analysis at the MetaboAnalyst 5.0. We used random forest analyses to establish the predictive model.
We recruited 220 malnourished and 198 non-malnourished patients with gastric cancer. The intensities of 25 annotated significantly differential metabolites were lower in patients with malnutrition than those without, while two others were higher in patients with malnutrition than those without, including newly identified significantly differential metabolites such as indoleacrylic acid and lysophosphatidylcholine(18:3/0:0). We matched eight metabolic pathways associated with malnutrition, including aminoacyl-tRNA biosynthesis, tryptophan metabolism, and glycerophospholipid metabolism. We established a predictive model with an area under the curve of 0.702 (95% CI: 0.651-0.768) based on four annotated significantly differential metabolites, namely indoleacrylic acid, lysophosphatidylcholine(18:3/0:0), L-tryptophan, and lysophosphatidylcholine(20:3/0:0).
We identified 27 specific differential metabolites of malnutrition in malnourished compared to non-malnourished patients with gastric cancer. We also matched eight corresponding metabolic pathways and developed a predictive model. These findings provide supportive data to better understand molecular mechanisms of malnutrition in patients with gastric cancer and new strategies for the prediction, diagnosis, prevention, and treatment for those malnourished.
尽管癌症患者中普遍存在营养不良，但营养不良的分子机制尚未完全阐明。本研究旨在鉴定显著差异代谢物，匹配相应的代谢途径，并开发胃癌患者营养不良的预测模型。
在这项横断面研究中，我们应用非靶向代谢组学方法，通过液相色谱-质谱联用技术，探索胃癌患者营养不良的血清指纹图谱。采用正交偏最小二乘判别分析和 t 检验鉴定营养不良特异的差异代谢物，并与人类代谢组数据库和脂质代谢物和途径策略相匹配。我们在 MetaboAnalyst 5.0 上通过途径分析匹配营养不良的相应代谢途径。我们使用随机森林分析建立预测模型。
我们招募了 220 名营养不良和 198 名非营养不良的胃癌患者。营养不良患者的 25 种鉴定出的显著差异代谢物的强度低于非营养不良患者，而另外两种代谢物在营养不良患者中的强度高于非营养不良患者，包括新鉴定的显著差异代谢物如吲哚丙烯酸和溶血磷脂酰胆碱(18:3/0:0)。我们匹配了 8 个与营养不良相关的代谢途径，包括氨酰-tRNA 生物合成、色氨酸代谢和甘油磷脂代谢。我们基于 4 种鉴定出的显著差异代谢物（吲哚丙烯酸、溶血磷脂酰胆碱(18:3/0:0)、L-色氨酸和溶血磷脂酰胆碱(20:3/0:0)）建立了一个预测模型，其曲线下面积为 0.702（95%CI：0.651-0.768）。
我们在营养不良的胃癌患者中鉴定出了 27 种与非营养不良患者相比的营养不良特异差异代谢物，还匹配了 8 个相应的代谢途径，并开发了一个预测模型。这些发现为更好地理解胃癌患者营养不良的分子机制以及为这些营养不良患者的预测、诊断、预防和治疗提供新策略提供了支持性数据。
