Prognostic Value of Salivary Biochemical Indicators in Primary Resectable Breast Cancer

Bel’skaya, Lyudmila V.; Sarf, Elena A.

doi:10.3390/metabo12060552

Cited by 4 publications

(6 citation statements)

References 63 publications

(70 reference statements)

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“…In the early stages of BC, the significantly higher levels of urea, as well as the lowered levels of total protein and uric acid, were observed. In a similar study on patients with primary resectable breast cancer (T 1-3 N 0-1 M 0 ), Bel’skaya and Sarf [ 36 ] found that salivary levels of diene conjugate below 3.93 c.u. before treatment could be significant risk factor for tumour recurrence (HR = 1.78, 95% CI: 1.02–3.08).…”

Section: Discussionmentioning

confidence: 99%

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Salivary Metabolomics for Systemic Cancer Diagnosis: A Systematic Review

et al. 2022

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Cancers are the leading cause of death worldwide. The most common cancers include breast, lung, and colorectum. Salivary metabolome profiling is a novel non-invasive method in oncological diagnosis. This systematic review was designed to answer the question “Are salivary metabolites reliable for the diagnosis of systemic cancers?”. Following the inclusion and exclusion criteria, nineteen studies were included (according to PRISMA statement guidelines). Changes in salivary metabolome were most commonly determined in patients with breast cancer, gastrointestinal cancers, and lung cancer. Most studies involved unstimulated whole saliva as the diagnostic material, evaluated by different spectroscopic methods. Among the found saliva metabolites, the alterations in the metabolic pathways of amino acids and polyamines were most frequently observed, which showed significant predictive values in oncological diagnostics. The most frequently encountered risks of bias were the absence of data regarding blinding, sample size justification, and randomisation. In conclusion, salivary metabolites seem to be potentially reliable for detecting the most common systemic cancers. However, further research is desirable to confirm these outcomes and to detect new potential metabolic biomarkers in saliva.

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Section: Discussionmentioning

confidence: 99%

“… Quality assessment, including the main potential risk of bias (risk level: green—low, yellow—unspecified, red—high; quality score: green—good, yellow—intermediate, red—poor) [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. …”

Section: Methodsmentioning

confidence: 99%

Salivary Metabolomics for Systemic Cancer Diagnosis: A Systematic Review

et al. 2022

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“…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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“…[4][5][6][7][8][9][10][11][12] Salivary amino acid proling has immense potential as a non-invasive diagnostic technique in cancer research. 13 Research has demonstrated that salivary amino acids can potentially serve as biomarkers for different cancers, and variations in amino acid concentrations in body uids play a crucial role in cancer diagnosis. 14 Analyzing amino acid patterns in saliva can help doctors enhance early diagnosis, prognosis, and patient outcomes, as well as contribute to the development of customised medicines.…”

Section: Introductionmentioning

confidence: 99%