Article-Journal | Simon Fontaine

Loss of salivary agglutinin induces changes in the salivary microbiome and accelerates development of oral cancer

Fri, 10 Apr 2026 00:00:00 +0000

Background: Salivary agglutinin, also known as deleted in malignant brain tumors 1 (DMBT1), is an anti-microbial protein. Salivary DMBT1 is low in saliva from patients with oral cancer and dramatically increases after treatment with accompanying microbial changes. While suppression of DMBT1 has been associated with oral microbial dysbiosis and oral cancer, direct effect has not been established.

Goal: To investigate the role of salivary agglutinin/DMBT1 in dysbiosis and oral cancer.

Methods: Microbiota were standardized between Dmbt1 knockout (Dmbt1 -/- ) and wild type (Dmbt1 +/+ ) mice by inter-breeding and co-housing. Oral cancer development was investigated using a carcinogen model. Saliva was collected at baseline, 4, 8, 12, 16, and 22 weeks after initiation of carcinogen, which was stopped at 16 weeks. Tongues were harvested for histopathology. The salivary microbiome was profiled via 16S rRNA gene sequencing. Longitudinal microbiota changes were determined using a locally sparse varying coefficient mixed model. The θ YC distance was calculated using nonmetric multidimensional scaling ordination.

Results: Carcinogen-treated Dmbt1 -/- had a higher prevalence of oral cancer with more aggressive invasion than Dmbt1 +/+ littermates. There were baseline microbiota differences between Dmbt1 +/+ and Dmbt1 -/- . Prevalence of Streptococcus in Dmbt1 +/+ and Brazyrhizobium in Dmbt1 -/- were increased in mice with precancerous lesions and cancer. Sphingonomas genus was predictive of transformation to cancer.

Conclusion: Loss of DMBT1 induces dysbiosis and accelerates oral cancer development.

Missing Value Imputation in Relational Data using Variational Inference

Tue, 20 May 2025 00:00:00 +0000

ADAPT: Analysis of Microbiome Differential Abundance by Pooling Tobit Models

Thu, 07 Nov 2024 00:00:00 +0000

Predicting Incident Adenocarcinoma of the Esophagus or Gastric Cardia Using Machine Learning of Electronic Health Records

Thu, 10 Aug 2023 00:00:00 +0000

Background and Context: Tools that can automatically predict incident esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) using electronic health records (EHR) to guide screening decisions are needed.

New Findings: In this retrospective case-control analysis using machine learning, the Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) Tool was well calibrated and more accurate than available alternatives. While GERD was associated with EAC/GCA, other factors added more information to the model.

Limitations: K-ECAN was developed and validated within the Veterans Health Administration (VHA) population. Non-Veterans may differ in terms of the frequency of medical encounters and laboratory blood draws, and non-VHA settings may differ in their practice of diagnostic coding.

Clinical Research Relevance: The Kettles Esophageal and Cardia Adenocarcinoma predictioN Tool is an automated, valid tool predicting incident esophageal

An adaptive multiple try Metropolis algorithm

Mon, 01 Aug 2022 00:00:00 +0000

A Unified Approach to Sparse Tweedie Modeling of Multisource Insurance Claim Data

Wed, 01 Jan 2020 00:00:00 +0000