Literature DB >> 27220894

Serum Level of Trefoil Factor 2 can Predict the Extent of Gastric Spasmolytic Polypeptide-Expressing Metaplasia in the H. pylori-Infected Gastric Cancer Relatives.

Hsin-Yu Kuo1,2, Wei-Lun Chang2, Yi-Chun Yeh1, Yu-Ching Tsai2,3,4, Chung-Tai Wu1,2, Hsiu-Chi Cheng2, Hsiao-Bai Yang5,6, Cheng-Chang Lu1,5, Bor-Shyang Sheu1,2.   

Abstract

BACKGROUND & AIMS: Gastric cancer has familial clustering in incidence, and the familial relatives of gastric cancer sufferers are prone to have spasmolytic polypeptide-expressing metaplasia (SPEM), and intestinal metaplasia (IM) after H. pylori infection. This study tested whether serum pepsinogen I/II and trefoil factor family (TFF) proteins can predict SPEM or IM in the H. pylori-infected relatives of patients with gastric cancer.
METHODS: We prospectively enrolled 119 H. pylori-infected relatives of gastric cancer patients of noncardiac gastric cancer patients, who then received panendoscopy to obtain gastric biopsy to define the presence of corpus gastritis index (CGI), SPEM, and IM. The advanced SPEM in histology was defined by TFF2 immunohistochemistry. Each patient also had checkups of serum TFF2, TFF3, and pepsinogen I/II by enzyme-linked immunosorbent assay (ELISA).
RESULTS: The 119 H. pylori-infected relatives included 61 with SPEM, and 34 with IM. The presence of either IM or SPEM was not related to the serum TFF2, TFF3, and pepsinogen I/II levels (p > .05). Serum TFF2 levels were higher in relatives with CGI who also had advanced SPEM (p = .032). For relatives without CGI, the elevated serum TFF2 levels correlated with higher H. pylori density and more severe gastritis in antrum (p = .001).
CONCLUSION: The serum TFF2 level cannot predict SPEM or IM in H. pylori-infected relatives of patients with gastric cancer. For H. pylori-infected relatives with CGI, serum TFF2 levels may predict the advanced severity of SPEM. Elevated serum TFF2 levels may indicate severe H. pylori-related inflammation, at risk of development or progression of SPEM in relatives without CGI.
© 2016 John Wiley & Sons Ltd.

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Keywords:  zzm321990H. pylorizzm321990; Diagnositc methods; gastric cancer; gastric metaplasia; serum; serum pepsinogens

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Year:  2016        PMID: 27220894     DOI: 10.1111/hel.12320

Source DB:  PubMed          Journal:  Helicobacter        ISSN: 1083-4389            Impact factor:   5.753


  2 in total

1.  Machine learning: A non-invasive prediction method for gastric cancer based on a survey of lifestyle behaviors.

Authors:  Siqing Jiang; Haojun Gao; Jiajin He; Jiaqi Shi; Yuling Tong; Jian Wu
Journal:  Front Artif Intell       Date:  2022-08-16

Review 2.  Precision Medicine Approaches to Prevent Gastric Cancer.

Authors:  Juntaro Matsuzaki; Hitoshi Tsugawa; Hidekazu Suzuki
Journal:  Gut Liver       Date:  2021-01-15       Impact factor: 4.519

  2 in total

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