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// Hua-Chuan Zheng 1 , Yu-Hong Qiu 1, 2 and Shuang Zhao 1 1 Department of Experimental Oncology and Animal Center, Shengjing Hospital of China Medical University, Shenyang 110004, China 2 Library, China Medical University, Shenyang 110004, China Correspondence to: Hua-Chuan Zheng, email: zheng_huachuan@hotmail.com Keywords: PTEN; gastric cancer; bibliometrics; meta analysis; bioinformatics analysis Received: August 01, 2017     Accepted: November 03, 2017     Published: January 02, 2018 ABSTRACT PTEN encodes a dual phospholipid phosphatase, and is frequently deleted, mutated or down-regulated in a variety of human malignancies. Here, we performed a systematic bibliometric, meta- and bioinformatics analysis through multiple online databases up to March 14, 2017. The co-citation and co-word analysis showed that the study about PTEN and gastric cancer mainly focused on PTEN discovery, correlation of its genetic and epigenetic alteration with cancers, the effects of PTEN expression on the phenotypes of gastric cancer cells, and the regulatory effects of miRNA on PTEN translation. Meta-analysis indicated that down-regulated PTEN expression was seen in gastric cancer in comparison to normal mucosa and dysplasia ( p < 0.05), and positively with depth of invasion, lymph node and distant metastasis, TNM staging, dedifferentiation and poor prognosis of gastric cancer ( p < 0.05). According to bioinformatics databases, PTEN mRNA expression was higher in gastric cancer than normal tissues ( p < 0.05), and positively correlated with depth of invasion and differentiation of gastric cancer ( p < 0.05). Kaplan-Meier plotter showed that a higher PTEN expression was positively correlated with overall and progression-free survival rates of all cancer patients, even stratified by aggressive parameters ( p < 0.05). These findings indicated that PTEN expression might be employed as a potential marker to indicate gastric carcinogenesis and subsequent progression, even prognosis.

The roles of PTEN expression in gastric cancer: a bibliometric, meta and bioinformatics analysis