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.2024 May;115(5):1695-1705.
doi: 10.1111/cas.16112. Epub 2024 Feb 28.

Salivary metabolomic biomarkers for non-invasive lung cancer detection

Affiliations

Salivary metabolomic biomarkers for non-invasive lung cancer detection

Naohiro Kajiwara et al. Cancer Sci.2024 May.

Abstract

Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.

Keywords: biomarker; lung cancer; metabolomics; plasma; saliva.

© 2024 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

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Conflict of interest statement

Masahiro Sugimoto is a board member of SalivaTech and serves as a consultant to Human Metabolome Technologies. The other authors have no conflict of interest. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Figures

FIGURE 1
FIGURE 1
Design of this study. (A) A comparative design of metabolomic profiles. (1) Comparison of tumor and paired control tissues collected from the patients with lung cancer. (2) Comparison of blood samples collected from the patients with lung cancer and control participants. (3) Comparison of saliva samples collected from the patients with lung cancer and control participants. (B) A flowchart of the discrimination model. All data were randomly split into training and validation datasets. Cross‐validation using training data to optimize the parameters of the model. The generalization ability of the developed model was evaluated using the validation data.
FIGURE 2
FIGURE 2
Metabolomic profile of tumor and paired control tissues. (A) Heatmap. A row and a column indicate a metabolite and a sample, respectively. Control and tumor samples are aligned at the left and right half of the heatmap. The paired samples are aligned from left to right (i.e., the most left sample in the control and the most left in tumor samples are paired). (B) Metabolomic pathway. Dot–line plots indicate the concentrations of paired‐control tissue (left) and tumor samples (right). Metabolites showing significant differences inp < 0.05 (FDR‐corrected paired Wilcoxon signed‐rank test) are indicated in yellow in the heatmap and indicated in red in the pathway. FDR, false discovery rate.
FIGURE 3
FIGURE 3
Metabolomic profiles in plasma samples. Control and tumor samples are aligned at the left and right half of the heatmap. The metabolite showing significant differences inp < 0.05 (FDR‐corrected Mann–Whitney test) is indicated in yellow. FDR, false discovery rate.
FIGURE 4
FIGURE 4
Metabolomic profiles in saliva samples. Control and tumor samples are aligned at the left and right half of the heatmap. The metabolites showing significant differences inp < 0.05 (FDR‐corrected Mann–Whitney test) are indicated in yellow. FDR, false discovery rate.
FIGURE 5
FIGURE 5
The discrimination ability of metabolites. (A) Venn diagram showing the number of metabolites with significant differences among three types of samples. The metabolites show significant differences between tumor and control samples at FDR‐correctedp < 0.05 (Mann–Whitney tests for plasma and saliva samples and paired Wilcoxon signed‐rank test for tumor samples). ROC curves of salivaryN1‐acetylspermidine for (B) training and (C) validation data to discriminate cancer samples from the control samples. ROC curves of the ADTree model using 12 salivary metabolites for (D) training and (E) validation data. ADTree, alternative decision tree; FDR, false discovery rate; ROC, receiver operating characteristic.
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