Integrative Predictive Modeling of Metastasis in Melanoma Cancer Based on MicroRNA, mRNA, and DNA Methylation Data

Kutlay A., AYDIN SON Y.

FRONTIERS IN MOLECULAR BIOSCIENCES, vol.8, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 8
  • Publication Date: 2021
  • Doi Number: 10.3389/fmolb.2021.637355
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Directory of Open Access Journals
  • Keywords: machine learning, melanoma, metastasis, metastatic molecular signatures, miRNA, mRNA, DNA methylation, LYMPH-NODE METASTASIS, GENE-EXPRESSION PROFILES, BIOMARKERS, CLASSIFICATION, PROGRESSION, NETWORKS, MIRNAS
  • Middle East Technical University Affiliated: Yes


Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regulatory impact of genetic biomarkers for monitoring metastatic molecular signatures of melanoma by investigating the consolidated effect of miRNA, mRNA, and DNA methylation.