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. 2025 Mar 8;15(6):660.
doi: 10.3390/diagnostics15060660.

Detection of Respiratory Disease Based on Surface-Enhanced Raman Scattering and Multivariate Analysis of Human Serum

Yulia Khristoforova  1 , Lyudmila Bratchenko  1 , Vitalii Kupaev  2 , Dmitry Senyushkin  3 , Maria Skuratova  4 , Shuang Wang  5 , Petr Lebedev  6 , Ivan Bratchenko  1
Affiliations

Detection of Respiratory Disease Based on Surface-Enhanced Raman Scattering and Multivariate Analysis of Human Serum

Yulia Khristoforova et al. Diagnostics (Basel). .

Abstract

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a significant public health concern, affecting millions of people worldwide. This study aims to use Surface-Enhanced Raman Scattering (SERS) technology to detect the presence of respiratory conditions, with a focus on COPD. Methods: The samples of human serum from 41 patients with respiratory diseases (11 patients with COPD, 20 with bronchial asthma (BA), and 10 with asthma-COPD overlap syndrome) and 103 patients with ischemic heart disease, complicated by chronic heart failure (CHF), were analyzed using SERS. A multivariate analysis of the SERS characteristics of human serum was performed using Partial Least Squares Discriminant Analysis (PLS-DA) to classify the following groups: (1) all respiratory disease patients versus the pathological referent group, which included CHF patients, and (2) patients with COPD versus those with BA. Results: We found that a combination of SERS characteristics at 638 and 1051 cm-1 could help to identify respiratory diseases. The PLS-DA model achieved a mean predictive accuracy of 0.92 for classifying respiratory diseases and the pathological referent group (0.85 sensitivity, 0.97 specificity). However, in the case of differentiating between COPD and BA, the mean predictive accuracy was only 0.61. Conclusions: Therefore, the metabolic and proteomic composition of human serum shows significant differences in respiratory disease patients compared to the pathological referent group, but the differences between patients with COPD and BA are less significant, suggesting a similarity in the serum and general pathogenetic mechanisms of these two conditions.

Keywords: chronic obstructive pulmonary disease (COPD); classification model; liquid biopsy; projection on latent structures with discriminant analysis (PLS-DA); respiratory diseases; surface-enhanced Raman scattering (SERS).

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Scheme of PLS-DA model building procedure.
Figure 2
Figure 2
The mean spectra with standard deviation (SD) of human serum of patients with different pathologies: (a) respiratory diseases vs. CHF; (b) different types of respiratory diseases: COPD, BA, and COPD&BA.
Figure 3
Figure 3
VIPs’ distribution for constructed PLS-DA models: (a) “respiratory diseases vs. CHF”; (b) COPD vs. BA.
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