title: Functional Annotation of Metastasis-associated MicroRNAs of Melanoma: A Meta-analysis of Expression Profiles.
abstract: BACKGROUND
  Melanoma is a type of cancer that develops from the pigment-containing cells. Until now, its pathological mechanisms remain largely unknown. The aim of this study was to identify metastasis-related microRNA (miRNAs) and gain an understanding of the biological functions in the metastasis of melanoma.
  
  
  METHODS
  We searched the PubMed and Gene Expression Omnibus database to collect miRNA expression profiling datasets about melanoma, with key words of "melanoma", "miRNA", "microarray", and "gene expression profiling". Only the original experimental works published before June 2016 for analyzing the metastasis of melanoma were retained, other nonhuman studies, reviews, and meta-analyses were removed. We performed a meta-analysis to explore the differentially expressed miRNA between metastatic and nonmetastatic samples. Moreover, we predicted target genes of the miRNAs to study their biological roles for these miRNAs.
  
  
  RESULTS
  We identified a total of 63 significantly differentially expressed miRNAs by meta-analysis of the melanoma expression profiling data. The regulatory network constructed by using these miRNAs and the predicted targets identified several key genes involved in the metastasis of melanoma. Functional annotation of these genes indicated that they are mainly enriched in some biological pathways such as mitogen-activated protein kinase signaling pathway, cell junction, and focal adhesion.
  
  
  CONCLUSIONS
  By collecting the miRNA expression datasets from different platforms, multiple biological markers were identified for the metastasis of melanoma. This study provided novel insights into the molecular mechanisms underlying this disease, thereby aiding the diagnosis and treatment of the disease.
authors.affiliation: Department of Stomatology, Chinese People's Liberation Army General Hospital, Beijing 100853, China.
authors.forename: Jing-Yi
authors.initials: JY
authors.lastname: Li
authors.affiliation: Department of Endocrinology and Metabolism, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China.
authors.forename: Li-Li
authors.initials: LL
authors.lastname: Zheng
authors.affiliation: Department of Endocrinology and Metabolism, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China.
authors.forename: Ting-Ting
authors.initials: TT
authors.lastname: Wang
authors.affiliation: Department of Stomatology, Chinese People's Liberation Army General Hospital, Beijing 100853, China.
authors.forename: Min
authors.initials: M
authors.lastname: Hu
mesh_terms: D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D006801:Humans; D008545:Melanoma / Q000235:genetics / Q000378:metabolism*; D035683:MicroRNAs / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D015398:Signal Transduction / Q000235:genetics / Q000502:physiology
chemical_list: D035683:MicroRNAs