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Overexpression of NREP Promotes Migration and Invasion in Gastric Cancer Through Facilitating Epithelial-Mesenchymal Transition.


ABSTRACT: The identification of biomarkers and effective therapeutic targets for gastric cancer (GC), the most common cause of cancer-related deaths around the world, is currently a major focus area in research. Here, we examined the utility of Neuronal Regeneration Related Protein (NREP) as a prognostic biomarker and therapeutic target for GC. We assessed the clinical relevance, function, and molecular role of NREP in GC using bioinformatics analysis and experimental validation. Our results showed that in GC, NREP overexpression was significantly associated with a poor prognosis. Our findings also suggested that NREP may be involved in the activation of cancer-associated fibroblasts and the epithelial-mesenchymal transition (EMT), with transforming growth factor β1 mediating both processes. In addition, NREP expression showed a positive correlation with the abundance of M2 macrophages, which are potent immunosuppressors. Together, these results indicate that NREP is overexpressed in GC and affects GC prognosis. Thus, NREP could be a prognostic biomarker and therapeutic target for GC.

SUBMITTER: Liu YJ 

PROVIDER: S-EPMC8565479 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Overexpression of NREP Promotes Migration and Invasion in Gastric Cancer Through Facilitating Epithelial-Mesenchymal Transition.

Liu Yuan-Jie YJ   Zeng Shu-Hong SH   Hu Yi-Dou YD   Zhang Yong-Hua YH   Li Jie-Pin JP  

Frontiers in cell and developmental biology 20211020


The identification of biomarkers and effective therapeutic targets for gastric cancer (GC), the most common cause of cancer-related deaths around the world, is currently a major focus area in research. Here, we examined the utility of Neuronal Regeneration Related Protein (<i>NREP</i>) as a prognostic biomarker and therapeutic target for GC. We assessed the clinical relevance, function, and molecular role of <i>NREP</i> in GC using bioinformatics analysis and experimental validation. Our results  ...[more]

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