Abstract
BackgroundCurrently, the main treatment for non-small cell lung cancer (NSCLC) is surgery and chemotherapy. Although major progress has been made in targeted treatment and immunotherapy, the survival rates for this disease are still low and associated with resistance to chemotherapy. Previous studies have shown that histone acetylation and microRNAs (miRNAs) might play an important role in chemotherapy resistance. The aim of this study was to identify candidate miRNAs related to cisplatin (DDP) resistance in lung adenocarcinoma. MethodsWe used 5-aza-2′-deoxycytidine and trichostatin A to reverse the drug resistance of A549/DDP cells in vitro, and miRNA expression profiling was performed by microarrays to identify candidate miRNAs. In addition, we investigated the correlations between miR-320a expression and clinical characteristics through data collected from Gene Expression Omnibus (GEO) microarrays, and The Cancer Genome Atlas (TCGA) to determine the clinical role of miR-320a in lung adenocarcinoma. Furthermore, we investigated the biological function of miR-320a. TargetScanHuman, PicTar2005 and miRanda v5.1. were used to predict the target genes of miR-320a; then, the function of these genes were suggested from the enrichment of GO categories items and KEGG analyses. ResultsTreatment with 5-Aza-dc significantly inhibited cellular proliferation, and increased apoptosis in the A549/DDP cells compared with the untreated cells. TSA did not reverse cisplatin resistance. MiR-320a was up-regulated during reversal of cisplatin resistance. The lung adenocarcinoma groups had a significantly lower level of miR-320a expression than the control groups. For the bioinformatics analyses, we found some target genes involved in cell cycle progression, tumor progression, the MAPK signaling pathway, and the ErbB signaling pathway. The promising target genes were highly enriched in various pathways in cancer. ConclusionsThe current study confirmed miR-320a was up-regulated during the revering of cisplatin resistance. The results of bioinformatics analyses may present a new method for investigating the pathogenesis of lung adenocarcinoma.
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