Analysis of the Metabolic Characteristics of Serum Samples in Patients With Multiple Myeloma

Haiwei Du, Linyue Wang, Bo Liu, Jinying Wang, Haoxiang Su, Ting Zhang, Zhongxia Huang, Haiwei Du, Linyue Wang, Bo Liu, Jinying Wang, Haoxiang Su, Ting Zhang, Zhongxia Huang

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.

Keywords: QExactiveTM Orbitrap MS; biomarkers; diagnosis; metabolome; multiple myeloma.

Figures

Figure 1
Figure 1
The workflow of our work. The Discovery phase (Cohort 1, 36 subjects), Validation phase (Cohort 2, 75 subjects) and Statistical (Cohort 2, 75 subjects) analyses phase were incorporated into our study. MM, multiple myeloma; AG, active MM patients also named as symptomatic myeloma; RG, MM patients responding to chemotherapy; HC, healthy controls.
Figure 2
Figure 2
PCA and OPLS-DA of the metabolic profiles of serum samples in HC, RG, and AG groups of Discovery phase analyzed by C18 and HILIC column chromatography. (A), PCA analysis in C18 mode; (B), OPLS-DA analysis in C18 mode; (C), PCA analysis in HILIC mode; (D), OPLS-DA analysis in HILIC mode. Red nodes, blue nodes and green nodes respectively represented the samples of the subjects in AG, RG, and HC groups. PCA: principal components analysis; OPLS-DA: orthogonal partial least-squares-discriminant analysis; HILIC: hydrophilic interaction liquid chromatography. The HC, RG, and AG groups in Discovery phase included 14, 10, 12 subjects, respectively.
Figure 3
Figure 3
Histogram showing the abundance of SCMs generated from C18 column chromatography in AG, RG, and HC groups of Discovery phase. (A), PC(18:3(6Z,9Z,12Z)/16:0); (B), PC(16:0/16:0); (C), LysoPE(0:0/16:0); (D), PC(18:0/16:0); (E), LysoPE(16:0/0:0); (F), LysoPE(18:1(11Z)/0:0); (G), SM(d18:0/16:1(9Z)); (H), PC(18:0/18:2(9Z,12Z)); (I), LysoPC(20:0)); (J), LysoPC(16:1(9Z)); (K), LysoPC(0:0/18:0); (L), LysoPC(P-18:0). *indicates P < 0.05, **indicates P < 0.01, and ***indicates P < 0.001. P < 0.05 indicates statistical significance. SCMs, significantly changed metabolites. The HC, RG, and AG groups in Discovery phase included 14, 10, 12 subjects, respectively.
Figure 4
Figure 4
Histogram showing the abundance of SCMs generated from HILIC column chromatography in AG, RG, and HC groups of Discovery phase. (A), 2-Hexenoylcarnitine; (B), 1-Methylhistidine; (C), Asymmetric dimethylarginine; (D), 5′-Methylthioadenosine; (E), Butyrylcarnitine; (F), N-Acetylputrescine; (G), Creatinine; (H), Valerylcarnitine; (I), 1-Methyladenosine; (J), DL-glutamate; (K), L-Octanoylcarnitine; (L), Decanoylcarnitine. *indicates P < 0.05, **indicates P < 0.01, and ***indicates P < 0.001. P < 0.05 indicates statistical significance. SCMs, significantly changed metabolites. The HC, RG, and AG groups in Discovery phase included 14, 10, 12 subjects, respectively.
Figure 5
Figure 5
Validation of the abundance of SCMs generated from C18 column chromatography in AG, RG, and HC groups in Validation phase. (A), PC(18:3(6Z,9Z,12Z)/16:0); (B), PC(16:0/16:0); (C), LysoPE(0:0/16:0); (D), PC(18:0/16:0); (E), LysoPE(16:0/0:0); (F), LysoPC(20:0)); (G), LysoPC(16:1(9Z)); (H), LysoPC(0:0/18:0); (I), LysoPC(P-18:0). *indicates P < 0.05, **indicates P < 0.01, and ***indicates P < 0.001. P < 0.05 indicates statistical significance. SCMs, significantly changed metabolites. Targeted metabolomics analyses was used to validate the abundance of SCMs in Validation phsge. The HC, RG, and AG groups in Validation phase included 20, 26, 29 subjects, respectively.
Figure 6
Figure 6
Validation of the abundance of SCMs generated from HILIC column chromatography in AG, RG, and HC groups in Validation phase. (A), 2-Hexenoylcarnitine; (B),1-Methylhistidine; (C), Asymmetric dimethylarginine; (D), 5′-Methylthioadenosine; (E), Butyrylcarnitine; (F), N-Acetylputrescine; (G), Creatinine; (H), Valerylcarnitine; (I), 1-Methyladenosine. *indicates P < 0.05, **indicates P < 0.01, and ***indicates P < 0.001. P < 0.05 indicates statistical significance. SCMs, significantly changed metabolites. Targeted metabolomics analyses was used to validate the abundance of SCMs in Validation phase. The HC, RG, and AG groups in Validation phase included 20, 26, 29 subjects, respectively.
Figure 7
Figure 7
The discriminatory ability of SCMs between MM patients and healthy controls was analyzed using an ROC curve in Validation phase. (A), PC(16:0/16:0); (B), PC(18:3(6Z,9Z,12Z)/16:0); (C), 2-Hexenoylcarnitine; (D), 1-Methyladenosine. ROC, receiver operating characteristic curve. ROC curves and AUC were obtained by SPSS22.0 software. The HC, RG, and AG groups in Validation phase included 20, 26, 29 subjects, respectively.
Figure 8
Figure 8
The discriminatory ability of SCMs between AG patients and RG patients was analyzed using an ROC curve in Validation phase. (A), PC(16:0/16:0); (B), PC(18:3(6Z,9Z,12Z)/16:0); (C), 2-Hexenoylcarnitine; (D), 1-Methylhistidine; (E), Asymmetric dimethylarginine; (F), 5′-Methylthioadenosine; (G), Butyrylcarnitine; (H), N-Acetylputrescine; (I), Creatinine; (J), Valerylcarnitine; (K), DL-Glutamate; (L), 3-Dehydroxycarnitine. ROC, receiver operating characteristic curve. ROC curves and AUC were obtained by SPSS22.0 software. The HC, RG, and AG groups in Validation phase included 20, 26, 29 subjects, respectively.
Figure 9
Figure 9
The survival analysis of identified SCMs in MM patients in Cohort 2. (A), 1-Methylhistidine; (B), Asymmetric dimethylarginine; (C), 5′-Methylthioadenosine; (D), Butyrylcarnitine; (E), N-Acetylputrescine; (F), Creatinine; (G), DL-Glutamate; (H), 3-Dehydroxycarnitine. Twenty-six Subjects of RG group and 29 subjects of AG groups in Cohort 2 was conduted to survival analyses. The cutoff value was calculated by ROC analysis and Youden index. Those 55 subjects were grouped into lower patients group and higher patients group based on the cutoff value. Patients with metabolite value less than cutoff value was incorporated into lower patient goup and those patients with metabolite value more than cutoff vuale was incorporated into higher paitent group, respectively. There was none of unit for the cutoff values of metabolites shown in Figure on account of the standard substance of metabolites identified in HILIC column is absent and we could not obtain the concentration of each of metabolites shown in Figure. The metabolites shown in Figure in each of samples was quantitative by the peak area generated from mass spectrometer.
Figure 10
Figure 10
The tumorigenesis model in MM. The interaction between reported signaling pathways and potential metabolic signaling pathways identified in our work which might be implicated in the pathogenesis of MM. Pink indicated elevated metabolites in the study; green indicated decreased metabolites in the study; red line indicated an inhibitory effect. MM, multiple myeloma.

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