Salivary Microbiota for Gastric Cancer Prediction: an exploratory study
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PROVIDER: PRJEB42657 | ENA |
REPOSITORIES: ENA
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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.
| S-EPMC7988213 | biostudies-literature
Project description:We have performed gene expression microarray analysis to profile transcriptomic signatures between cancer and noncancerous patients Gastric cancer is currently the second leading cause of cancer deaths. Due to the difficulty of diagnosing patients in the early stages of gastric cancer, it is critical to develop a method that can diagnose the disease at the early stage to allow for better treatment options. In this study, we discovered salivary transcriptomic and miRNA biomarkers for the detection of gastric cancer and identified there are mRNA-miRNA correlations in saliva. RNA was extracted from saliva supernatant and mRNA candidates were identified that can distinguish gastric cancer from non-gastric cancer patients
2015-01-15 | E-GEOD-64951 | biostudies-arrayexpress
Project description:We have performed gene expression microarray analysis to profile transcriptomic signatures between cancer and noncancerous patients Gastric cancer is currently the second leading cause of cancer deaths. Due to the difficulty of diagnosing patients in the early stages of gastric cancer, it is critical to develop a method that can diagnose the disease at the early stage to allow for better treatment options. In this study, we discovered salivary transcriptomic and miRNA biomarkers for the detection of gastric cancer and identified there are mRNA-miRNA correlations in saliva.
2015-01-15 | GSE64951 | GEO
Project description:In spite of the many recent developments in the field of vector sialomics, the salivary glands of larvalmosquitoes have been largely unexplored. We used whole-transcriptome microarray analysis to create a gene-expression profile of the salivary gland tissue of fourth-instar Anopheles gambiae larvae, and compare it to the gene-expression profile of a matching group of whole larvae. We identified a total of 221 probes with expression values that were (a) significantly enriched in the salivary glands, and (b)sufficiently annotated as to allow the prediction of the presence/absence of signal peptides in their corresponding gene products. Based on available annotation of the protein sequences associated with these probes, we propose that the main roles of larval salivary secretions include: (a) immune response, (b) mouthpart lubrication, (c) nutrient metabolism, and (d) xenobiotic detoxification. Other highlights of the study include the cloning of a transcript encoding a previously unknown salivary defensin (AgDef5), the confirmation of mucus secretion by the larval salivary glands, and the first report of salivary lipocalins in the Culicidae. Keywords: Anopheles gambiae, salivary gland, Diptera, gene expression, salivary defensin, transcriptome, salivary lipocalin
2008-05-01 | GSE9642 | GEO
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.
2025-11-10 | PXD062318 | Pride
Project description:Sequencing of 16S ribosomal RNA (rRNA) gene, which has improved the characterization of microbial community, has made it possible to detect a low level Helicobacter pylori (HP) sequences even in HP-negative subjects which were determined by a combination of conventional methods. This study was conducted to obtain a cutoff value for HP colonization in gastric mucosa biopsies and gastric juices by the pyrosequencing method. Corresponding author: Department of Internal Medicine, Seoul National University Bundang Hospital, Seoungnam, Gyeonggi-do, Korea; Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea (Tel., +82-31-787-7008; e-mail, nayoungkim49@empas.com). Microbial DNA from gastric mucosal samples [gastric antrum (n=63, mucosal biopsy), follow-up sample on gastric antrum (n=16, mucosal biopsy), and gastric body (n=18, mucosal biopsy)] and gastric juices (n=4, not mucosal biopsy) was amplified by nested PCR using universal bacterial primers, and the 16S rRNA genes were pyrosequenced.
2014-09-18 | E-GEOD-61493 | biostudies-arrayexpress
Project description:Salivary microbiota in periodontal diseases
| PRJEB61123 | ENA
Project description:Salivary microbiota in periodontal diseases
| PRJEB79725 | ENA
Project description:The primary objectives of this study are:
* To identify clinical or histological factors associated with gastric cancer development in patients with IM and AG
* To establish a machine learning algorithm for prediction of future gastric cancer risks and individual risk stratification in patient with IM and AG
| 2373934 | ecrin-mdr-crc
Project description:gastric mucosa microbiota Raw sequence reads
| PRJNA1032279 | ENA