Measuring cancer driving force of chromosomal aberrations through multi-layer Boolean implication networks

PLoS One. 2024 Apr 9;19(4):e0301591. doi: 10.1371/journal.pone.0301591. eCollection 2024.

Abstract

Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents the advantage of using COMBO (Combining Multi Bio Omics) to suggest a new role of the chromosomal aberration as a cancer driver factor. Exploiting the heterogeneous multi-layer networks, COMBO integrates gene expression and DNA-methylation data in order to identify complex bilateral relationships between transcriptome and epigenome. We evaluated the multi-layer networks generated by COMBO on different TCGA cancer datasets (COAD, BLCA, BRCA, CESC, STAD) focusing on the effect of a specific chromosomal numerical aberration, broad gain in chromosome 20, on different cancer histotypes. In addition, the effect of chromosome 8q amplification was tested in the same TCGA cancer dataset. The results demonstrate the ability of COMBO to identify the chromosome 20 amplification cancer driver force in the different TCGA Pan Cancer project datasets.

MeSH terms

  • Chromosome Aberrations*
  • DNA Methylation
  • Epigenome
  • Humans
  • Neoplasms* / genetics
  • Neoplasms* / metabolism
  • Transcriptome

Grants and funding

AP, SA, AF, have been partially supported by the following research project: PO-FESR Sicilia 2014-2020 “DiOncoGen: Innovative diagnostics” (CUP G89J18000700007). AP, has been also partially supported by the following research project: “PROMOTE: Identificazione di nuovi biomarcatori per la diagnosi precoce di mesotelioma maligno pleurico in soggetti ex esposti a fibre asbestiformi”, University of Catania - Piano di incentivi per la ricerca 2020-2022. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.