Mapping Conformational Changes in the Saliva Proteome Potentially Associated with Oral Cancer Aggressiveness.

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.

Connecting multiple microenvironment proteomes uncovers the biology in head and neck cancer.

The poor prognosis of head and neck cancer (HNC) is associated with metastasis within the lymph nodes (LNs). Herein, the proteome of 140 multisite samples from a 59-HNC patient cohort, including primary and matched LN-negative or -positive tissues, saliva, and blood cells, reveals insights into the biology and potential metastasis biomarkers that may assist in clinical decision-making. Protein profiles are strictly associated with immune modulation across datasets, and this provides the basis for investigating immune markers associated with metastasis. The proteome of LN metastatic cells recapitulates the proteome of the primary tumor sites. Conversely, the LN microenvironment proteome highlights the candidate prognostic markers. By integrating prioritized peptide, protein, and transcript levels with machine learning models, we identify nodal metastasis signatures in blood and saliva. We present a proteomic characterization wiring multiple sites in HNC, thus providing a promising basis for understanding tumoral biology and identifying metastasis-associated signatures.

Meta-omics analysis indicates the saliva microbiome and its proteins associated with the prognosis of oral cancer patients.

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.

Peptidomics-Driven Strategy Reveals Peptides and Predicted Proteases Associated With Oral Cancer Prognosis.

Protease activity has been associated with pathological processes that can lead to cancer development and progression. However, understanding the pathological unbalance in proteolysis is challenging because changes can occur simultaneously at protease, their inhibitor, and substrate levels. Here, we present a pipeline that combines peptidomics, proteomics, and peptidase predictions for studying proteolytic events in the saliva of 79 patients and their association with oral squamous cell carcinoma (OSCC) prognosis. Our findings revealed differences in the saliva peptidome of patients with (pN+) or without (pN0) lymph-node metastasis and delivered a panel of ten endogenous peptides correlated with poor prognostic factors plus five molecules able to classify pN0 and pN+ patients (area under the receiver operating characteristic curve > 0.85). In addition, endopeptidases and exopeptidases putatively implicated in the processing of differential peptides were investigated using cancer tissue gene expression data from public repositories, reinforcing their association with poorer survival rates and prognosis in oral cancer. The dynamics of the OSCC-related proteolysis were further explored via the proteomic profiling of saliva. This revealed that peptidase/endopeptidase inhibitors exhibited reduced levels in the saliva of pN+ patients, as confirmed by selected reaction monitoring-mass spectrometry, while minor changes were detected in the level of saliva proteases. Taken together, our results indicated that proteolytic activity is accentuated in the saliva of patients with OSCC and lymph-node metastasis and, at least in part, is modulated by reduced levels of salivary peptidase inhibitors. Therefore, this integrated pipeline provided better comprehension and discovery of molecular features with implications in the oral cancer metastasis prognosis.