Comparative analyses of salivary exosomal miRNAs for patients with or without lung cancer

Liu, Mengfeng; Yu, Xiran; Bu, Jianlong; Xiao, Qifan; Ma, Sitong; Chen, Naozhong; Qu, Changfa

doi:10.3389/fgene.2023.1249678

Cited by 6 publications

(1 citation statement)

References 44 publications

(44 reference statements)

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“… 10 The miRNA profile in salivary exosomes was shown to distinguish patients with lung cancer from healthy controls. 11 Salivary amino acids could be used to assess the risk of various types of cancer. 12 Although these markers have shown potential for lung cancer diagnosis, the relationship between saliva and organs far from the oral cavity needs to be comprehensively clarified.…”

Section: Introductionmentioning

confidence: 99%

Salivary metabolomic biomarkers for non‐invasive lung cancer detection

Kajiwara,

Kakihana,

Maeda

et al. 2024

Cancer Science

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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.

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Section: Introductionmentioning

confidence: 99%