12 February 2016 : Laboratory Research
Identification of Molecular Targets for Predicting Colon Adenocarcinoma
Yansheng WangABCDEF, Jun ZhangABCDE, Li LiABCDE, Xin XuABCD, Yong ZhangABCD, Zhaowei TengBCDE, Feihu WuABCDEFGDOI: 10.12659/MSM.895881
Med Sci Monit 2016; 22:460-468
Abstract
BACKGROUND: Colon adenocarcinoma mostly happens at the junction of the rectum and is a common gastrointestinal malignancy. Accumulated evidence has indicated that colon adenocarcinoma develops by genetic alterations and is a complicated disease. The aim of this study was to screen differentially expressed miRNAs (DEMs) and genes with diagnostic and prognostic potentials in colon adenocarcinoma.
MATERIAL AND METHODS: In this study we screened DEMs and their target genes (DEGs) between 100 colon adenocarcinoma and normal samples in The Cancer Genome Atlas (TCGA) database by using the DEseq toolkit in Bioconductor. Then Go enrichment and KEGG pathway analysis were performed on the selected differential genes by use of the DAVID online tool. A regulation network of miRNA-gene was constructed and analyzed by Cytoscape. Finally, we performed ROC analysis of 8 miRNAs and ROC curves were drawn.
RESULTS: A total of 159 DEMs and 1921 DEGs were screened, and 1881 pairs of miRNA-target genes with significant negative correlations were also obtained. A regulatory network of miRNA-gene, including 60 cancer-related genes and 47 miRNAs, was successfully constructed. In addition, 5 clusters with several miRNAs regulating a set of target genes simultaneously were identified through cluster analysis. There were 8 miRNAs involved in these 5 clusters, and these miRNAs could serve as molecular biomarkers to distinguish colon adenocarcinoma and normal samples indicated by ROC analysis.
CONCLUSIONS: The identified 8 miRNAs were closely associated with colon adenocarcinoma, which may have great clinical value as diagnostic and prognostic biomarkers and provide new ideas for targeted therapy.
Keywords: Biomarkers, Tumor - genetics, Adenocarcinoma - metabolism, Cluster Analysis, Colonic Neoplasms - metabolism, Gene Expression Profiling, MicroRNAs - genetics, ROC Curve
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