Goblet Cell Ratio in Combination with Differentiation and Stem Cell Markers in Barrett Esophagus Allow Distinction of Patients with and without Esophageal Adenocarcinoma.
Goblet Cell Ratio in Combination with Differentiation and Stem Cell Markers in Barrett Esophagus Allow Distinction of Patients with and without Esophageal Adenocarcinoma.
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DOI:
10.1158/1940-6207.capr-16-0117
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发表时间:
2017-01
期刊:
影响因子:
--
通讯作者:
Quante M
中科院分区:
文献类型:
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作者:
Schellnegger R;Quante A;Rospleszcz S;Schernhammer M;Höhl B;Tobiasch M;Pastula A;Brandtner A;Abrams JA;Strauch K;Schmid RM;Vieth M;Wang TC;Quante M
The rising incidence of esophageal adenocarcinoma (EAC) is mirrored by the increasing prevalence of Barrett’s Esophagus (BE), a precursor lesion resulting in a large number of individuals “at risk” for this lethal malignancy. Among BE patients only ~0.3% annually will develop EAC. Since large numbers of patients are followed in endoscopic surveillance, there is a need for risk prediction among a growing population of BE patients. We identified 4 potential biomarkers from an inflammation (IL-1β)-dependent mouse model of Barrett’s esophagus and tested them in 189 BE patients with and without HGD/early cancer (T1). The primary goal was to distinguish BE patients with no evidence of dysplasia from those with dysplasia. Increasing stem cell marker LGR5 and niche cell marker DCLK1 and decreasing differentiation marker (secretory mucus cells, TFF2+ cells) correlated with elevated tumor score in the mouse. Having outlined the origin of those markers in the BE mouse model we showed the applicability for human BE: We compared 96 patients with non-dysplastic BE tissue to 97 patients with BE and HGD or early cancer. Low levels of TFF2 (AUC 87.2%) provided the best discrimination between non-dysplastic BE and BE with cancer, followed by high levels of DCLK1 (AUC 83.4%), low GC ratio (AUC 79.4%) and high LGR5 (AUC 71.4%). The GC ratio, rather than the presence of GCs per se, was found to be an important discriminator. These findings may be useful in developing future risk prediction models for BE patients and ultimately to improve EAC surveillance.