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Objective. This Work is aimed at exploring the effect of microRNA (MiR)-608 on the function of nonsmall cell lung cancer (NSCLC) A549 cells and related mechanisms. Methods. Blood samples of 106 NSCLC patients (experimental group) as well as 124 normal people (control group) were selected for relevant investigation. Polymerase chain reaction (PCR) as well as DNA sequencing was used to determine the genotyping of the MiR-608 rs4919510 polymorphism. MiR-608 expression in cells was detected by real-time PCR technology. Western blotting was used to detect changes in protein levels. NSCLC tissues as well as adjacent tissues were explored in 33 patients undergoing surgery. Results. MiR-608 rs4919510 does not influence the incidence of NSCLC patients. In addition, MiR-608 expression was downregulated in the tumor tissue of NSCLC patients, while the transcription factor activating enhancer-binding protein 4 (TFAP4) expression was upregulated. MiR-608 promotes DOX- (Doxorubicin-) induced apoptosis by negatively regulating TFAP4 expression in NSCLC tissue. TFAP4 can significantly inhibit the migration of A549 cells. Conclusion. The findings in this investigation can contribute to the effective treatment of NSCLC patients. Also, the investigation can provide some theoretical support for the application of new targets for NSCLC treatment. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer-associated mortality worldwide. The aim of the present study was to identify the differentially expressed genes (DEGs) and enriched pathways in lung cancer by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid microarray data of 31 pairs of lung cancer tissues and matched normal samples (GSE19804) were obtained from the Gene Expression Omnibus database. Significance analysis of the gene expression profile was used to identify DEGs between cancer tissues and normal tissues, and a total of 1,970 DEGs, which were significantly enriched in biological processes, were screened. Through the Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, 77 KEGG pathways associated with lung cancer were identified, among which the Toll-like receptor pathway was observed to be important. Protein-protein interaction network analysis extracted 1,770 nodes and 10,667 edges, and identified 10 genes with key roles in lung cancer with highest degrees, hub centrality and betweenness. Additionally, the module analysis of protein-protein interactions revealed that 'chemokine signaling pathway', 'cell cycle' and 'pathways in cancer' had a close association with lung cancer. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of lung cancer, and certain genes (including advanced glycosylation end-product specific receptor and epidermal growth factor receptor) may be used as candidate target molecules to diagnose, monitor and treat lung cancer. [ABSTRACT FROM AUTHOR]

Introduction: Lung adenocarcinoma (LAC) accounts for more than a half of non-small cell lung cancer with high morbidity and mortality. Progression of treatment has not accelerated the improvement of its prognosis. Hence, it is an urgent need to develop novel biomarkers for its early diagnosis and treatment. Materials and methods: In this study, we proposed to identify LAC survival-related genes through comprehensive analysis of large-scale gene expression profiles. LAC gene expression data sets were obtained from The Cancer Genome Atlas (TCGA). Identification of differentially expressed genes (DEGs) in LAC compared with adjacent normal lung tissues was first performed followed by univariate Cox regression analysis to obtain genes that are significantly associated with LAC survival (SurGenes). Then, we conducted sure independence screening (SIS) for SurGenes to identify more reliable genes and the prognostic signature for LAC survival prediction. Another two lung cancer data sets from TCGA and Gene Expression Omnibus (GEO) were used for the validation of prognostic signature. Results: A total of 20 genes were obtained, which were significantly associated with the overall survival (OS) of LAC patients. The prognostic signature, a weighted linear combination of the 20 genes, could successfully separate LAC samples with high OS from those with low OS and had robust predictive performance for survival (training set: p-value ,2.2×10-16; testing set: p-value =2.04×10-5, area under the curve (AUC) =0.615). Combined with GEO data set, we obtained four genes, that is, FUT4, SLC25A42, IGFBP1, and KLHDC8B that are found in both the prognostic signature and DEGs of LAC in GEO data set. Discussion: The prognostic signature combined with multi-gene expression profiles provides a moderate OS prediction for LAC and should be helpful for appropriate treatment method selection. [ABSTRACT FROM AUTHOR]