kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme
Kings Coll London, Ctr Host Microbiome Interact, Fac Dent Oral & Craniofacial Sci, London SE1 9RT, England..
KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-2851-9651
KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-8301-9959
KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-3721-8586
Show others and affiliations
2021 (English)In: International Journal of Molecular Sciences, ISSN 1661-6596, E-ISSN 1422-0067, Vol. 22, no 24, p. 13213-, article id 13213Article in journal (Refereed) Published
Abstract [en]

Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.

Place, publisher, year, edition, pages
MDPI AG , 2021. Vol. 22, no 24, p. 13213-, article id 13213
Keywords [en]
glioblastoma, genome-scale metabolic models, multi-omics data, systems biology
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:kth:diva-307167DOI: 10.3390/ijms222413213ISI: 000738537400001PubMedID: 34948010Scopus ID: 2-s2.0-85120671378OAI: oai:DiVA.org:kth-307167DiVA, id: diva2:1632713
Note

QC 20220127

Available from: 2022-01-27 Created: 2022-01-27 Last updated: 2023-12-07Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records

Altay, ÖzlemLi, XiangyuZhang, ChengUhlén, MathiasShoaie, SaeedMardinoglu, Adil

Search in DiVA

By author/editor
Altay, ÖzlemLi, XiangyuZhang, ChengUhlén, MathiasShoaie, SaeedMardinoglu, Adil
By organisation
Science for Life Laboratory, SciLifeLab
In the same journal
International Journal of Molecular Sciences
Cancer and Oncology

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 32 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf