Project description:To characterize the salivary microbiota in patients at different progressive histological stages of gastric carcinogenesis and identify microbial markers for detecting gastric cancer, two hundred and ninety-three patients were grouped into superficial gastritis (SG; n = 101), atrophic gastritis (AG; n = 93), and gastric cancer (GC; n = 99) according to their histology. 16S rRNA gene sequencing was used to access the salivary microbiota profile. A random forest model was constructed to classify gastric histological types based on the salivary microbiota compositions. A distinct salivary microbiota was observed in patients with GC when comparing with SG and AG, which was featured by an enrichment of putative proinflammatory taxa including Corynebacterium and Streptococcus. Among the significantly decreased oral bacteria in GC patients including Haemophilus, Neisseria, Parvimonas, Peptostreptococcus, Porphyromonas, and Prevotella, Haemophilus, and Neisseria are known to reduce nitrite, which may consequently result in an accumulation of carcinogenic N-nitroso compounds. We found that GC can be distinguished accurately from patients with AG and SG (AUC = 0.91) by the random forest model based on the salivary microbiota profiles, and taxa belonging to unclassified Streptophyta and Streptococcus have potential as diagnostic biomarkers for GC. Remarkable changes in the salivary microbiota functions were also detected across three histological types, and the upregulation in the isoleucine and valine is in line with a higher level of these amino acids in the gastric tumor tissues that reported by other independent studies. Conclusively, bacteria in the oral cavity may contribute gastric cancer and become new diagnostic biomarkers for GC, but further evaluation against independent clinical cohorts is required. The potential mechanisms of salivary microbiota in participating the pathogenesis of GC may include an accumulation of proinflammatory bacteria and a decline in those reducing carcinogenic N-nitroso compounds.

Project description:Background: Gastric cancer (GC) is often associated with a poor prognosis, due to its asymptomatic early stages. The current gold standard for diagnosing GC, upper endoscopy, is invasive and has limited sensitivity for detecting gastric preneoplasia such as dysplasia. Non-invasive biomarkers, such as circulating proteins in the blood, hold promise for the early detection of asymptomatic gastric lesions. Methods: During an exploratory study, plasma samples from 39 participants, including patients diagnosed with different gastric pathologies (gastritis, preneoplasia, GC) and healthy controls, were analyzed using mass spectrometry-based proteomics. Differentially abundant proteins were identified through pairwise comparisons and sparse Partial Least Squares Discriminant Analysis (sparse PLS-DA). Fifteen candidate proteins were selected and quantified by ELISA in plasma samples from the full cohort of 138 participants. Results: Proteomic profiling identified 691 proteins in plasma samples. Pairwise comparisons highlighted up to 213 candidate biomarkers distinguishing cancer patients from healthy controls, while distinguishing gastritis and preneoplasia proved challenging due to their similar proteomes. Sparse PLS-DA identified 85 proteins that distinguish all patient groups. Subsequent ELISA validation confirmed Leptin as a promising biomarker for detecting gastric preneoplasia, particularly in women. Nine additional proteins (ATAD3B, IGFALS, JUP, LBP, MAN2A1, ARG1, CA2, HPT, KRT14) showed differential plasma levels across patients groups, influenced by age and gender. Multi-biomarker prediction models that incorporated factors such as age, gender, and H. pylori status, outperformed single-protein models. Using 4-fold cross-validation repeated 10 times, the best-performing models achieved high predictive accuracy, with mean AUROC=85.3% for classifications cancer vs. non-cancer and 83.9% for cancer/preneoplasia vs. healthy/gastritis. On the full cohort, the best biomarker combinations reached AUROC values exceeding 94% for these classifications. Conclusions: This study introduces a novel, non-invasive approach for predicting GC based on plasma protein concentrations across a broad spectrum of gastric pathologies. Predictive models, validated through robust cross-validation, demonstrated high accuracy using a limited panel of biomarkers. By relying on simple blood sampling, this strategy holds promise for high-risk gastric mucosal lesions, including at asymptomatic stages. Such an approach could significantly improve early detection and clinical management of GC, offering direct benefit for patients outcomes.

Project description:Background: Schizophrenia (SZ) is a chronic, severe mental disorder that presents significant challenges to diagnosis and effective treatment. Emerging evidence suggests that gut microbiota may play a role in the disease's pathogenesis. However, fewer studies have directly investigated the potential links between oral microbiota and SZ. Purpose: This study aimed to explore the relationship between salivary microbiota dysbiosis and SZ, examining microbial and metabolic alterations that may contribute to SZ pathophysiology. Methods: Salivary samples from 30 hospitalized patients diagosed with SZ and 10 healthy controls were collected. The microbial and metabolic profiles were analyzed using 16S rRNA gene sequencing and metabolomic profiling. Clinical parameters, including oral health status, were also evaluated to minimize variability in sampling. Results: Patients with SZ exhibited significantly poorer oral health compared to healthy controls, with more missing teeth and worse periodontal status. Microbiota sequencing revealed notable alterations in the overall structure and composition of the salivary microbiome in SZ patients, characterized by increased abundance of specific genera such as Neisseria and Porphyromonas. Metabolomic analysis indicated significant differences between the SZ and control groups, with upregulation of key metabolic pathways, including “β-alanine metabolism” and “vitamin digestion and absorption”. Correlations between microbial dysbiosis and elevated levels of certain metabolites, such as L-methionine sulfoxide (L-MetO) and tyramine, were observed, suggesting links to oxidative stress. Conclusion: The study highlights the presence of significant dysbiosis and metabolic dysfunction in the salivary microbiota of SZ patients, suggesting that alterations in the oral microbiome may contribute to SZ pathogenesis. These results provide new insights into potential diagnostic biomarkers and therapeutic targets for SZ. Further studies with larger sample sizes are required to validate these findings.

Project description:Objectives: Arterial hypertension (AH) influences salivary gland physiology and oral health, being associated with a higher incidence of periodontal disease in pregnant women. Evidence points to a bidirectional relationship between the oral microbiota and blood pressure regulation. Therefore, this study aimed to characterize the oral health of pregnant women and AH-associated changes in the salivary proteome and microbiome during pregnancy and postpartum. Design: Ten healthy women and ten women with AH were enrolled. Saliva was collected during pregnancy and six months postpartum. The salivary proteome was characterized by shotgun label-free mass spectrometry analysis. Specific proteins were validated through parallel reaction monitoring (PRM). The oral microbiota was characterized via 16S rRNA gene amplicon sequencing (V4 region). The periodontal health and the caries history was assessed during pregnancy. Results: Pregnant women with AH had lower junction plakoglobin (JUP)- and desmoplakin (DSP)-specific peptide levels than healthy women, confirmed by the PRM approach. The levels of these proteins correlated negatively with periodontal health indexes, which were higher in pregnant women with AH. In AH, nitrate-reducing microorganisms had lower abundance, correlating positively with JUP and DSP-specific peptides. Conclusions: The salivary proteome and microbiota are shaped by AH during and after pregnancy. Further research is required to understand the underlying mechanisms impairing oral health in AH. Data acquisition has been supported by EPIC-XS, project number 393, funded by the Horizon 2020 program of the European Union and the National Institute for Neurological Research (Programme EXCELES, ID Project No. LX22NPO5107) and the MEYS/EU project OP JAK-MULTIOMICS_CZ - Multi-omics platform for the search for biological correlates of diseases and the development of new diagnostic, preventive and therapeutic procedures (CZ.02.01.01/00/23_020/0008540).

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Salivary Microbiota for Gastric Cancer Prediction: an exploratory study

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