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Diverse proteomics-based strategies have been applied to saliva to quantitatively identify diagnostic and prognostic targets for oral cancer. Considering that these targets may be regulated by events that do not imply variation in protein abundance levels, we hypothesized that changes in protein conformation can be associated with diagnosis and prognosis, revealing biological processes and novel targets of clinical relevance. For this, we employed limited proteolysis-mass spectrometry in saliva samples to explore structural alterations, comparing the proteome of healthy control and oral squamous cell carcinoma (OSCC) patients with and without lymph node metastasis. Thirty-six proteins with potential structural rearrangements were associated with clinical patient features including transketolase and its interacting partners. Moreover, N -glycosylated peptides contribute to structural rearrangements of potential diagnostic and prognostic markers. Altogether, this approach utilizes saliva proteins to search for targets for diagnosing and prognosing oral cancer and can guide the discovery of potential regulated sites beyond protein-level abundance.

Saliva is a biofluid that maintains the health of oral tissues and the homeostasis of oral microbiota. Studies have demonstrated that Oral squamous cell carcinoma (OSCC) patients have different salivary microbiota than healthy individuals. However, the relationship between these microbial differences and clinicopathological outcomes is still far from conclusive. Herein, we investigate the capability of using metagenomic and metaproteomic saliva profiles to distinguish between Control (C), OSCC without active lesion (L0), and OSCC with active lesion (L1) patients. The results show that there are significantly distinct taxonomies and functional changes in L1 patients compared to C and L0 patients, suggesting compositional modulation of the oral microbiome, as the relative abundances of Centipeda, Veillonella, and Gemella suggested by metagenomics are correlated with tumor size, clinical stage, and active lesion. Metagenomics results also demonstrated that poor overall patient survival is associated with a higher relative abundance of Stenophotromonas, Staphylococcus, Centipeda, Selenomonas, Alloscordovia, and Acitenobacter. Finally, compositional and functional differences in the saliva content by metaproteomics analysis can distinguish healthy individuals from OSCC patients. In summary, our study suggests that oral microbiota and their protein abundance have potential diagnosis and prognosis value for oral cancer patients. Further studies are necessary to understand the role of uniquely detected metaproteins in the microbiota of healthy and OSCC patients as well as the crosstalk between saliva host proteins and the oral microbiome present in OSCC., (Copyright © 2021. Published by Elsevier B.V.)

Aims: Evaluate the abundance of the selected targets, alpha-1-antitrypsin (A1AT) and macrophage migration inhibitory factor (MIF), and correlate these findings with the risk of developing severe oral mucositis (OM)., Materials and Methods: Head and neck squamous cell carcinoma (HNSCC) patients submitted to radiotherapy (RT) or chemoradiotherapy (CRT) were assessed. OM grade and pain were evaluated daily during treatment. Two protein targets, A1AT and MIF, were evaluated, using selected reaction monitoring-mass spectrometry (SRM-MS), in whole saliva, collected prior to oncologic treatment. The results obtained from the targeted proteomic analysis were correlated with OM clinical outcomes., Results: A total of 27 patients were included, of whom 21 (77.8%) had locally advanced disease (clinical stage III or IV). Most patients (70.4%) received CRT. OM grades 2 (40.8%) and 3 (33.3%) were the most prevalent during RT with a mean highest reported OM-related pain of 3.22 through the visual analogue scale (VAS). The abundance of A1AT and MIF correlated significantly with severe (grades 3 or 4, p < 0.02) compared with moderate-low (grades 1 or 2, p < 0.04) OM grade., Conclusions: There is a correlation between the abundance of salivary A1AT and MIF and oncologic treatment-induced OM. The correlation of MIF expression with severe OM appears to be compatible with its physiological pro-inflammatory role. These results open up great possibilities for the use of salivary MIF and A1AT levels as prognostic markers for effective therapeutic interventions, such as photobiomodulation therapy, patient-controlled analgesia, or personalized medicaments.