Identification of neoplasm-specific signatures of miRNA interactions by employing a systems biology approach

Bonab R. A., Asfa S., Kontou P., KARAKÜLAH G., PAVLOPOULOU A.

PEERJ, cilt.10, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.7717/peerj.14149
  • Dergi Adı: PEERJ
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, EMBASE, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: miRNA interactions, Neoplasms, Bioinformatics, Network analysis, Mutual exclusiv-ity, Modules, Diagnosis, CANCER, MICRORNAS, FAMILY, CLUSTER, DATABASE, ASSOCIATION, PROGRESSION, EXPRESSION, BIOMARKER, NETWORKS
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases.