Identification of Salivary Proteomic Biomarkers for Oral Cancer Screening

Ishikawa, Shinya; Ishizawa, Kenichi; Tanaka, Atsushi; Kimura, Hirohito; Kitabatake, Kenichiro; Sugano, Akira; Edamatsu, Kaoru; Ueda, Shohei; Iino, Mitsuyoshi

doi:10.21873/invivo.12289

Cited by 20 publications

(17 citation statements)

References 29 publications

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Section: Resultsmentioning

confidence: 99%

“…However, peptide analysis can present several problems that affect the detector response due to unwanted interference from the matrix or from reagents and other additives used to facilitate protein digestion . Thus, an alternative to eliminate these interfering signals is using C18 spin columns after tryptic digestion, which removes interfering contaminants and releases peptides in MS-compatible solutions, resulting in greater sensitivity and a high-quality spectrum …”

Section: Resultsmentioning

confidence: 99%

“…27 Thus, an alternative to eliminate these interfering signals is using C18 spin columns after tryptic digestion, which removes interfering contaminants and releases peptides in MS-compatible solutions, resulting in greater sensitivity and a high-quality spectrum. 28 After the saliva digestion protocol, it was possible to observe the spectral differences between an unprepared saliva sample (Figure 1A) and another sample after the protein digestion step (Figure 1B). This allows to make a comparison of the ions detected by the MALDI FT-ICR MS analysis, giving indications of some peptides already identified in saliva studies in the literature (Table 1).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19

et al. 2022

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Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 μL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: ■ Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19

et al. 2022

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“…The major and minor salivary glands secrete an abundance of proteins belonging to classes such as the proline-rich proteins, α-amylases, defensins, mucins, salivary cystatins, and histatins [ 106 , 107 ]. Thousands of salivary proteins have been identified and quantified in a variety of diseases including oral cancer, head and neck squamous cell carcinoma, and Sjögren’s syndrome using mass spectrometry techniques [ 108 , 109 , 110 , 111 , 112 ]. It has been suggested that saliva contains approximately 40% of the potential protein biomarkers found in cancer, stroke, and cardiovascular disease [ 106 ].…”

Section: Salivamentioning

confidence: 99%

“…In addition, unlike the serum/plasma proteome, issues associated with high-abundance proteins can be avoided by using serum-free media during cell culture. However, a potential drawback of conditioned media as a source of biomarker discovery is that the in vitro cell culturing methods do not completely reflect the in vivo tumour environment [ 109 ].…”

Section: Bone Marrow Conditioned Mediamentioning

confidence: 99%

Clinical Proteomics of Biofluids in Haematological Malignancies

Dunphy

O’Mahoney

Dowling

et al. 2021

IJMS

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Since the emergence of high-throughput proteomic techniques and advances in clinical technologies, there has been a steady rise in the number of cancer-associated diagnostic, prognostic, and predictive biomarkers being identified and translated into clinical use. The characterisation of biofluids has become a core objective for many proteomic researchers in order to detect disease-associated protein biomarkers in a minimally invasive manner. The proteomes of biofluids, including serum, saliva, cerebrospinal fluid, and urine, are highly dynamic with protein abundance fluctuating depending on the physiological and/or pathophysiological context. Improvements in mass-spectrometric technologies have facilitated the in-depth characterisation of biofluid proteomes which are now considered hosts of a wide array of clinically relevant biomarkers. Promising efforts are being made in the field of biomarker diagnostics for haematologic malignancies. Several serum and urine-based biomarkers such as free light chains, β-microglobulin, and lactate dehydrogenase are quantified as part of the clinical assessment of haematological malignancies. However, novel, minimally invasive proteomic markers are required to aid diagnosis and prognosis and to monitor therapeutic response and minimal residual disease. This review focuses on biofluids as a promising source of proteomic biomarkers in haematologic malignancies and a key component of future diagnostic, prognostic, and disease-monitoring applications.

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Salivary analysis: An emerging paradigm for non‐invasive healthcare diagnosis and monitoring

Liao

Chen

2023

Interdisciplinary Medicine

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As an emerging non-invasive and easy-to-use toolkit, salivary analysis has garnered significant attention recently. By leveraging the latest discoveries in salivary biomarkers, salivary analysis has the potential to revolutionize the field of healthcare by providing valuable information for monitoring an individual's health status, diagnosing disease progression, and enabling personalized treatment. However, the transitional development of breakthrough salivary analysis remains an intriguing challenge. Only a few innovations have progressed to new transformative healthcare diagnosis and monitoring tools. In this review, we attempt to illustrate a bird's eye view of the emerging field of salivary analysis by exploring an assorted selection of seminar progress that can potentially shape the developing trajectory of the field of salivary analysis. The fundamental nature of saliva and the versatile robust uses of salivary analysis as a novel non-invasive method in various healthcare diagnostic and monitoring fields will be thoroughly discussed. To inspire next-stage development in this thriving and fruitful field, we also crystalize key findings, pinpoint challenges that remain to be addressed and present an outlook on the future transitional development of salivary analysis, invigorating future research of groundbreaking non-invasive healthcare diagnosis and monitoring strategies, and beyond.

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Identification of Salivary Proteomic Biomarkers for Oral Cancer Screening

Cited by 20 publications

References 29 publications

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19

Clinical Proteomics of Biofluids in Haematological Malignancies

Salivary analysis: An emerging paradigm for non‐invasive healthcare diagnosis and monitoring

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