Metabolomics analysis reveals novel serum metabolite alterations in cancer cachexia

Abstract

Background: 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.

Methods: 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 t-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.

Results: 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.

Conclusion: 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.

Keywords: GC-MS metabolomics; body metabolism; cancer cachexia; erythronic acid; glucuronic acid; metabolic pathways; serum metabolites.

Figure 1

Violin plots of body mass index (BMI) in cachectic (n = 42) and non-cachectic (n = 78) patients. BMI differed significantly between cachectic and non-cachectic cancer patients (p < 0.001, two-tailed unpaired t-test).

Figure 2

Exploratory multivariate statistical analysis. (A) Partial least square discriminant analysis (PLS-DA) score plots distinct clustering of cachectic (red) and non-cachectic (green) cancer patients. (B) The PLS-DA model was evaluated for its validity using a random permutation test that involved 100 permutations. The plot generated after the test highlighted the best classifier (a red asterisk) with an R2 value of 0.69, indicating the amount of variance explained by the model, and a Q2 value of 0.48, which indicated its predictive ability. A high R2 and Q2 value indicates good predictive ability and confirms the validity of the PLS-DA model. The accuracy of the best model is summarized in an inset table, which includes Q2, R2, and the number of components used in the model. “Comps” refer to the number of components.

Figure 3

Box-and-whisker and dot plots showing significant differences in serum levels of specific metabolites between cachectic and non-cachectic cancer patients. Specific significant metabolite differences were obtained after Tukey’s HSD and illustrated as normalized peak area differences. *P ≤ 0.05; **P ≤ 0.01; *** P ≤ 0.0001 and all lower values.

Figure 4

Heatmap showing the 38 metabolites with significant differences in serum level between cachectic and non-cachectic cancer patients. Significant differences were determined by false discovery rate (FDR)-corrected t-test p-values (FDR-corrected p < 0.05) to adjust for multiple hypothesis testing. The colors from green to red indicate increased metabolite concentration (normalized peak area).

Figure 5

Volcano plot displaying the distribution of significantly altered metabolites with identification in a group comparison between cachectic and non-cachectic cancer patients. Red represents significantly up-regulated metabolites, blue represents significantly down-regulated metabolites, and grey represents metabolites with no difference in comparative analysis between cachectic and non-cachectic cancer patients. Metabolites with a t-test p-value less than 0.05 were selected, and the results were adjusted for multiple hypothesis testing using the false discovery rate (FDR).

Figure 6

Topology map of altered metabolic pathways, which describes the impact of metabolites selected from a comparative t-test (p-value < 0.05), adjusted for multiple hypotheses testing by false discovery rate (FDR). The top ten altered metabolic pathways in the cancer cachexia group are 1. aminoacyl tRNA biosynthesis, 2. valine, leucine and isoleucine metabolism, 3. glutathione metabolism, 4. valine, leucine and isoleucine degradation, 5. arginine biosynthesis, 6. alanine, aspartate and glutamate metabolism, 7. phenylalanine, tyrosine, and tryptophan metabolism, 8. glyoxylate and dicarboxylate metabolism, 9. glycine, serine, and threonine metabolism, and 10. arginine and proline metabolism.

Figure 7

ROC curve of predictive machine learning (ML) models (Simple Logistics) for binominal discrimination between cachectic and non-cachectic state. List below ROC curve shows the confusion matrix and accuracy value of the model.