AUTHOR=Du Haiwei , Wang Linyue , Liu Bo , Wang Jinying , Su Haoxiang , Zhang Ting , Huang Zhongxia
TITLE=Analysis of the Metabolic Characteristics of Serum Samples in Patients With Multiple Myeloma
JOURNAL=Frontiers in Pharmacology
VOLUME=9
YEAR=2018
URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2018.00884
DOI=10.3389/fphar.2018.00884
ISSN=1663-9812
ABSTRACT=Aims: This study aimed to identify potential, non-invasive biomarkers for diagnosis and monitoring of the progress in multiple myeloma (MM) patients.
Methods: MM patients and age-matched healthy controls (HC) were recruited in Discovery phase and Validation phase, respectively. MM patients were segregated into active group (AG) and responding group (RG). Serum samples were collected were conducted to non-targeted metabolomics analyses. Metabolites which were significantly changed (SCMs) among groups were identified in Discovery phase and was validated in Validation phase. The signaling pathways of these SCMs were enriched. The ability of SCMs to discriminate among groups in Validation phase was analyzed through receiver operating characteristic curve. The correlations between SCMs and clinical features, between SCMs and survival period of MM patients were analyzed.
Results: Total of 23 SCMs were identified in AG compared with HC both in Discovery phase and Validation phase. Those SCMs were significantly enriched in arginine and proline metabolism and glycerophospholipid metabolism. 4 SCMs had the discriminatory ability between MM patients and healthy controls in Validation phase. Moreover, 12 SCMs had the ability to discriminate between the AG patients and RG patients in Validation phase. 10 out of 12 SCMs correlated with advanced features of MM. Moreover, 8 out of 12 SCMs had the negative impact on the survival of MM. 5′-Methylthioadenosine may be the only independent prognostic factor in survival period of MM.
Conclusion: 10 SCMs identified in our study, which correlated with advanced features of MM, could be potential, novel, non-invasive biomarkers for active disease in MM.