Statistical Analysis of Long-Chain Non-Coding RNA SNHG1 Influencing the Proliferation of Non-Small Cell Lung Cancer A549 Cells by Competitive Binding to microRNA-101

Abstract

Objective: To explore the effect of LncRNA SNHG1 on the proliferation of non-small cell lung cancer A549 cells by competitive binding to miR-101 using bioinformatics analysis. Methods: We used quantitative real-time PCR analyzed the differential expression of SNHG1 in non-small cell lung to analyze cancer tissues, adjacent nontumorous tissues, non-small cell lung cancer cell lines (A549, SPC-A1) and normal human lung epithelial cells (BEAS-2B). Transient transfected A549 cells with siRNA SNHG1 plasmid and detected the proliferation and apoptosis of cells in vitro or in vivo by MTT assay, apoptosis test and model tumor of athymic mouse. SNHG1 plasmid vector containing miR-101 predicted binding site to investigate whether SNHG1 was a functional target of miR-101 by luciferase reporter assay. The interaction between SNHG1 and miR-101 on the proliferation of A549 cells was investigated by transfection of miR-101-ih and then monitored the expression of SNHG1. Results: We found that expression of SNHG1 was up-regulated in NSCLC tissues and A549 cell lines. Furthermore, function assays showed that SNHG1 inhibition suppressed NSCLC cell proliferation both in vitro and outside vivo, and accelerated the apoptosis in vitro. We also found that the luciferase activity was significantly decreased by the co-transfection of miR-101-mim and SNHG1-Wt. In addition, we found that inhibition of SNHG1 led to significantly increased expression of miR-101. The expression of SNHG1 and the proliferation of A549 cells in cells that transfected with miR-101-ih was significantly higher than control group. Conclusions: LncRNA SNHG1 had a direct interaction with miR-101, and miR-101 was an inhibitory target for SNHG1 in NSCLC progression. The inhibition of miR-101 could enhanced the expression of SNHG1 and promoted the proliferation of A549 cells. This can be verified using bioinformatics analysis.