Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings

Seema Sandeep Redekar (Lead / Corresponding author), Satishkumar L. Varma, Atanu Bhattacharjee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
10 Downloads (Pure)

Abstract

Background: Glioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. GBM patients usually have a poor prognosis. Identification of genes associated with the progression of the disease is essential to explain the mechanisms or improve the prognosis of GBM by catering to targeted therapy. It is crucial to develop a methodology for constructing a biological network and analyze it to identify potential biomarkers associated with disease progression. 

Methods: Gene expression datasets are obtained from TCGA data repository to carry out this study. A survival analysis is performed to identify survival associated genes of GBM patient. A gene co-expression network is constructed based on Pearson correlation between the gene’s expressions. Various topological measures along with set operations from graph theory are applied to identify most influential genes linked with the progression of the GBM. 

Results: Ten key genes are identified as a potential biomarkers associated with GBM based on centrality measures applied to the disease network. These genes are SEMA3B, APS, SLC44A2, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, CTSZ, and KRTAP4.2. Higher expression values of two genes, SLC44A2 and KRTAP4.2 are found to be associated with progression and lower expression values of seven gens SEMA3B, APS, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, and CTSZ are linked with the progression of the GBM. Conclusions: The proposed methodology employing a network topological approach to identify genetic biomarkers associated with cancer.

Original languageEnglish
Article number22
JournalJournal of the Egyptian National Cancer Institute
Volume35
Issue number1
DOIs
Publication statusPublished - 24 Jul 2023

Keywords

  • Cancer progression
  • Correlation
  • Genetic biomarker
  • Graph theory
  • Network topology
  • Survival

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Fingerprint

Dive into the research topics of 'Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings'. Together they form a unique fingerprint.

Cite this