Dataset Information

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

ABSTRACT: Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed.We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees.Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy.Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information.Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR.

SUBMITTER: Sun X

PROVIDER: S-EPMC4873373 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

Sun Xiangqing X Elston Robert C RC Barnholtz-Sloan Jill S JS Falk Gary W GW Grady William M WM Faulx Ashley A Mittal Sumeet K SK Canto Marcia M Shaheen Nicholas J NJ Wang Jean S JS Iyer Prasad G PG Abrams Julian A JA Tian Ye D YD Willis Joseph E JE Guda Kishore K Markowitz Sanford D SD Chandar Apoorva A Warfe James M JM Brock Wendy W Chak Amitabh A

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20160229 5

<h4>Background</h4>Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factor ...[more]

PMID: 26929243

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Project description:As the premalignant lesion of human esophageal adenocarcinoma (EAC), Barrett's esophagus (BE) is characterized by intestinal metaplasia in the normal esophagus (NE). Gene expression profiling with microarray and serial analysis of gene expression (SAGE) may help us understand the potential molecular mechanism of human BE.We analyzed three microarray datasets (two cDNA arrays and one oligonucleotide array) and one SAGE dataset with statistical tools, significance analysis of microarrays (SAM) and SAGE(Poisson), to identify individual genes differentially expressed in BE. Gene set enrichment analysis (GSEA) was used to identify a priori defined sets of genes that were differentially expressed. These gene sets were grouped according to either certain signaling pathways (GSEA curated), or the presence of consensus binding sequences of known transcription factors (GSEA motif). Immunohistochemical staining (IHC) was used to validate differential gene expression.Both SAM and SAGE(Poisson) identified 68 differentially expressed genes (55 BE genes and 13 NE genes) with an arbitrary cutoff ratio (> or =4-fold). With IHC on matched pairs of NE and BE tissues from 6 patients, these genes were grouped into 6 categories: category I (25 genes only expressed in BE), category II (5 genes only expressed in NE), category III (8 genes expressed more in BE than in NE), and category IV (2 genes expressed more in NE than in BE). Differential expression of the remaining genes was not confirmed by IHC either due to false discovery (category V), or lack of proper antibodies (category VI). Besides individual genes, the TGFbeta pathway and several transcription factors (CDX2, HNF1, and HNF4) were identified by GSEA as enriched pathways and motifs in BE. Apart from 9 target genes known to be up-regulated in BE, IHC staining confirmed up-regulation of 19 additional CDX1 and CDX2 target genes in BE.Our data suggested an important role of CDX1 and CDX2 in the development of BE. The IHC-confirmed gene list will lead to future studies on the molecular mechanism of BE.

Dataset Information

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

Publications

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

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