Effect of Mirna Administration on Non-Small Cell Lung Cancer Cells Studied by Cellular Viability Assay and Atr-Ftir Spectroscopy Combined With Multivariate Data-Analysis

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Abstract

MicroRNAs (miRNAs), small non-coding RNAs, play a significant role in the regulation of gene expression by various mechanisms. Some miRNAs such as hsa-miR-145 (mir145), hsa-let-7a-1 (let7), hsa-miR-155 (mir155), and hsa-miR-29b (mir29b) are expressed at low levels in cancers and associated with proliferation, metastasis, invasion and apoptosis. In the current study, we aimed to investigate the effect of selected synthetic miRNAs and their combinations on the non-small cell lung cancer (NSCLC) cells (A549) by following the cell viability profile and alterations in the cellular biomolecules with biophysical features. After administration of commercial miRNAs and their various combinations to A549 cell line, each group was analyzed with cell viability assay and attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy combined with unsupervised multivariate analysis. Bioinformatics analysis was also performed to detect and to classify the target human genes obtained from the mirDB database. According to the cell viability results, the "mir29b + let7" combination and "mir155" significantly decreased the cancer cell viability whereas the "mir145 + mir29b" and "mir155 + mir145" combinations dramatically increased the cancer cell viability when compared to the control cells. The FTIR data revealed that administration of the "mir155", "mir29b + let7 + mir155", and "mir29b + let7" combinations caused a decrease in the contents of proteins, lipids and nucleic acids in A549 cells. This study suggests that those miRNA combinations might be potential targets for vaccines or miRNA-based therapies that can restore the miRNA activity and thus should be further evaluated to combat lung cancer with miRNA technology.

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Karabay Yavasoglu, N.Ulku/0000-0002-7483-0184

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Microrna, A549 Cells, Atr-Ftir Spectroscopy, Bioinformatics, Multivariate Statistical Analysis, Principal Component Analysis

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209

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