MiR-22-3p Expression is down-regulated in lung adenocarcinoma
Background: Our current study was performed with an attempt to detect the expression of microRNA-22-3p (miR-22-3p) in lung adenocarcinoma, as well as to analyze its role in clinical practice. In addition, its relationship with vascular endothelial growth factor (VEGF) and metastasis related indexes was focused. Material and Method: The trials in which 62 cases of lung adenocarcinoma were received to collect tumor tissue (study group) and normal lung tissue (control group) were eligible for this study. The expression of miR-22-3p in the two groups was detected through RT-PCR. Immunohistochemical method was used to detect the expression of VEGF and leukocyte differentiation antigen 31 (CD31) marked microvessel density (MVD) in lung adenocarcinoma. The expressions of matrix metalloproteinase-3 (MMP-3) and matrix metalloproteinase-7 (MMP-7) in lung adenocarcinoma were also detected through the use of Western Blot. Results: The present study revealed significant difference in the expression of miR-22-3p between the two groups. No significant difference in the expression of gender, age, neural invasion and the number of lesions were observed between groups. There was significant difference in the expression of miR-22-3p in the maximum diameter of tumor, pleural recidivism, vascular recidivism, lymph node metastasis and different TNM stages. Based on survival analysis, miR-22-3p was linked to survival time. Correlation analysis indicated that there was negative correlation between miR-22-3p and VEGF, miR-22-3p and MVD, miR-22-3p and MMP-3, and miR-22-3p and MMP-7 in lung adenocarcinoma. Conclusion: Our findings provide evidence that miR-22-3p is low expressed in lung adenocarcinoma tissues and the low expression of miR-22-3p is closely associated with clinicopathological characteristics and the prognosis. MiR-22-3p may be involved in the tumor progression of lung adenocarcinoma and may serve as a biomarker for the diagnosis and prognosis of lung adenocarcinoma.
Copyright (c) 2021 Dong-Jie Ma, Xiao-Yun Zhou, Ying-Zhi Qin, Zhen-Huan Tian, Hong-Sheng Liu, Shan-Qing Li
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