Background:ARID3B (AT-rich interaction domain 3B) has been demonstrated to be associated with the progression and patient prognosis of several human tumors. We conducted the present study to investigate the biological behavior and clinical relevance of ARID3B in gastric cancer (GC).
Methods:Detection of the expression level in GC tissues and cell lines were performed by Western blot and immunohistochemistry. We also retrospectively analyzed the correlation of ARID3B with clinicopathological characteristics and patient prognosis in gastric cancer. The biological functions of ARID3B in GC cells were further explored by transwell migration assays, wound healing assays and cell proliferation assay.
Results:The present study suggested that the expression of ARID3B was significantly lower in GC tissues than in adjacent normal tissues. IHC staining in tissues of 406 GC patients from training and validation sets verified that ARID3B over-expression correlated with clinicopathological features, such as degree of differentiation and clinical stage. Meanwhile, ARID3B was proved to be an independent prognostic factor for GC prognosis. Furthermore, over-expression of ARID3B suppressed proliferation in GC cells according CCK8 assay. We found that over-expression of ARID3B inhibited GC cell migration by transwell assay and wound healing assay. Furthermore, EMT markers were detected in ARID3B over-expression GC cells, which showed that ARID3B may inhibit metastasis of GC cells.
Conclusion:Our results firstly revealed that the expression level of ARID3B was closely correlated with clinicopathological features and may serve as an independent prognostic factor for GC patients. More importantly, ARID3B could suppress GC progression, including cell proliferation, migration and metastasis.
Keywords:ARID3B; gastric cancer; prognosis; progression.
Over-Expression of ARID3B Suppresses Tumor Progression and Predicts Better Prognosis in Patients With Gastric Cancer
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
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细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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