Variability of serum novel serum peptide biomarkers correlates with the disease states of multiple myeloma

Abstract

Background: The bone marrow microenvironment provides an optimal substrate for multiple myeloma (MM) initiation and progression. The soluble component of MM niche is dynamic with the disease states of MM. We formerly employed proteomic profiling to construct a MM model. Four peptides constituting the model were selected by supervised neural network algorithm (SNN).

Methods: 62 Newly diagnosed (ND) MM and 64 healthy controls (HCs) were picked up for validating the distinguishing capability of the SNN model. Nano-liquid chromatography-electrospray ionization-tandem mass spectrometry was used for peptide identification. MM in different disease states and HCs were choosed for peptides relative intensities comparison. Western blot and ELISA were employed to validate the variability.

Results: The sensitivity and specificity of the independent testing data set for blind validation were 93.55% and 92.19%. The relative intensities of three out of the four peptides were increased in ND and refractory and relapse patients but decreased to that level of HCs in complete remission and very good partial remission patients. Relative intensity of the remaining peptide was negatively associated with MM remission. The peptides sequencing results showed that they were derived from dihydropyrimidinase-like 2, fibrinogen alpha chain, platelet factor 4 and alpha-fetoprotein.

Conclusions: The potential value of the four peptides in monitoring MM treatment response was arised from their close correlation with MM disease states.

Keywords: Biomarkers; Bone marrow microenvironment; Multiple myeloma; Proteomic profiling; Treatment response.

Fig. 1

Serum specimens from 62 ND MM and 64 HCs were used for SNN model blind validation. 58 cases of MM were correctly assigned to MM group (93.55% sensitivity). 59 HCs were correctly identified as the non-diseased, 5 cases had been wrongly judged as MM (92.19% specificity). Green circle: HCs; Red cross: MM ND patients; Black dot: MM ND patients in validation group; Blue dot: HCs in validation group

Fig. 2

MS/MS map of peptide with MW of 2660.65 Da. a The enlarged picture of peptide with MW of 2660.65 Da. b The b and y ions spectra used to identify the peptide with MW of 2660.65 Da. c The sequence of the peptide with MW of 2660.65 Da

Fig. 3

MS/MS map of peptide with MW of 2990.4 Da. a The enlarged picture of peptide with MW of 2990.4 Da. b The b and y ions spectra used to identify the peptide with MW of 2990.4 Da. c The sequence of the peptide with MW of 2990.4 Da

Fig. 4

MS/MS map of peptide with MW of 3315.96 Da. a The enlarged picture of peptide with MW of 3315.96 Da. b The b and y ions spectra used to identify the peptide with MW of 3315.96 Da. c The sequence of the peptide with MW of 3315.96 Da

Fig. 5

MS/MS map of peptide with MW of 7763.24 Da. a The enlarged picture of peptide with MW of 7763.24 Da. b The b and y ions spectra used to identify the peptide with MW of7763.24 Da. c The sequence of the peptide with MW of 7763.24 Da

Fig. 6

Validation of protein fragment by Western blot analyses. a Expression levels of the fibrinogen alpha chain and dihydropyrimidinase-like 2 differ among the four groups of HC, MM-ND, and MM-RR. PF4 immunoreactive bands show that weak or no band was detected in ND and RR MM cases. b Densitometry comparison of PF4 protein relative to β-actin as determined by Western blot analyses in (a). c Densitometry comparison of dihydropyrimidinase-like 2 protein relative to β-actin as determined by Western blot analyses in (a). d Densitometry comparison of fibrinogen alpha chain protein relative to β-actin as determined by Western blot analyses in (a)

Fig. 7

Correlation analysis between PF4 and platelet count as well as VEGF. a Correlation analysis between serum PF4 and platelet count in ND MM (r = 0.179; p = 0.165). b Correlation analysis between serum PF4 and serum VEGF in ND MM (r = − 0.960; p = 1.108e−014)