Preliminary exploration of the metabolic profile and metabolic pathways in newly diagnosed multiple myeloma / 中华肿瘤杂志

Objective: To explore the metabolite profile and metabolic pathways of newly diagnosed multiple myeloma (MM). Methods: Gas chromatography-mass spectrometry (GC-MS) was employed for the high-throughput detection and identification of serum samples from 55 patients with MM and 37 healthy controls matched for age and sex from 2016 to 2017 collected at the First Affiliated Hospital of Soochow University. The relative standard deviation (RSD) of quality control (QC) samples was employed to validate the reproducibility of GC-MS approach. The differential metabolites between patients with MM and healthy controls were detected by partial least squares discrimination analysis (PLS-DA), and t-test with false discovery rate (FDR) correction. Metabolomics pathway analysis (MetPA) was employed to construct metabolic pathways. Results: There were 55 MM patients, including 34 males and 21 females. The median age was 60 years old (42-73 years old). There were 30 cases of IgG type, 9 cases of IgA type, 1 case of IgM type, 2 cases of non-secreted type, 1 case of double clone type and 12 cases of light chain type, including 3 cases of kappa light chain type and 9 cases of lambda light chain type. The result of QC sample test showed that the proportion of compounds with the RSD of the relative content of metabolites < 15% was 70.21% obtained by the reproducibility of GC-MS experimental data, which implied that the experimental data were reliable. A total of 17 metabolites were screened differently with the healthy control group, including myristic acid, hydroxyproline, cysteine, palmitic acid, L-leucine, stearic acid, methionine, phenylalanine, glycerin, serine, isoleucine, tyrosine, valine, citric acid, inositol, threonine, and oxalic acid (VIP>1, P<0.05). Metabolic pathway analysis suggested that metabolic disorders in MM patients comprised mainly phenylalanine metabolism, glyoxylic acid and dicarboxylic acid metabolism, phosphoinositide metabolism, cysteine and methionine metabolism, glycerolipid metabolism, glycine, serine, and threonine metabolism. Conclusion: Compared with normal people, patients with newly diagnosed MM have obvious differences in metabolic profiles and metabolic pathways.

Metabolomics of Chronic Myelogenous Leukemia Based on GC-MS / 中国实验血液学杂志

<p><b>OBJECTIVE</b>To investigate the metabolism characteristics, to search for potential biomarkers associated with disease and to explore related metabolic pathways by analyzing the plasma metabolic profile of patients with chronic myelogenous leukemia (CML) through metabolomies.</p><p><b>METHODS</b>Twenty-six newly diagnosed CML patients in the First Affilated Hospital of Soochow University from February 2015 to April 2015, 26 allogeneic hematopoietic stem cell donors as healthy controls and 26 patients treated with tyrosine kinase inhibitors (TKI) to obtain the best efficacy as post-treatment controls were enrolled in this study. All the metabolites of plasma were extracted by Gas Chromatography-Mass Spectrometer(GC-MS) to collect metabolic fingerprint. Multivariate pattern recognition analysis and t test were combined to screen out the metabolic biomarkers at different time points. The receiver operating characteristic curve analysis was used to evaluate the clinical efficacy of metabolites, and the metabolic pathway analysis was performed.</p><p><b>RESULTS</b>Significantly different metabolite expression mode was found seen between CML and healthy control groups. Six changed metabolites in CML were confirmed by multivariate and variate statistical analyses. Compared with the healthy controls, the levels of tetradecanoic acid and glycerol were decreased, the lactic acid, myo-inositol, d-galactose and glycine in CML patients also increased (all VIP>1,P<0.05, AUC>0.7). The plasma metabolites in CML patients after treatment with tyrosine kinase inhibitors (TKI) showed a recovery trend toward to normal levels. The plasma metabolic pathways of CML were mainly related with galactose, pyruvate, glycerolipid, inositol phosphate and glycine, serine and threonine metabolism (all impact value>0.10).</p><p><b>CONCLUSION</b>Significant changes in plasma metabolite levels were found in CML patients. Metabolomics combined with multivariate pattern recognition analysis may be a new tool to assist diagnosis.</p>