Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages—834 NL, 711 MGUS, and 970 MM spectra—and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527–1530 ${\mathrm{cm}}^{-1}$ as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.

Raman spectroscopy is a non-destructive spectroscopic technique that provides complex molecular information. It is used to examine the physiological and pathological responses of living cells, such as differentiation, malignancy, and inflammation. The responses of two cellular states, initial and full-blown inflammation, have mainly been investigated using a comparative analysis with Raman spectra. However, the tipping point of the inflammatory state transition remains unclear. Therefore, the present study attempted to identify the tipping point of inflammation using a cell model. We stimulated RAW264.7 mouse macrophages with lipopolysaccharide (LPS) and continuously collected Raman spectra every 2 h for 24 h from the initial and full-blown inflammation states. A Partial Least Squares analysis and Principal Component Analysis—Linear Discriminant Analysis predicted the tipping point as 14 h after the LPS stimulation. In addition, a Dynamical Network Biomarker (DNB) analysis, identifying the tipping point of a state transition in various phenomena, indicated that the tipping point was 14 h and identified tryptophan as a biomarker. The results of a multivariate analysis and DNB analysis show the cellular tipping point.